1,140 Fluid Mechanics jobs in India

What you will do

 Build computational models and simulations using proprietary and vendor tools to enhance design and operational practices and address evolving business needs.

Design, build and execute parametric studies using simulation models to provide engineering design recommendations for business decisions. Conduct research and development on new technologies, methods, or best practices.

Work with wells engineers, drilling and completion experts, computational and data scientists, and laboratory scientists across the globe to enhance understanding of subsurface technologies and tools.

 Create tutorials, documentation, training material or case studies. Prepare technical reports, presentations, and documentation for internal and external stakeholders.

Mentor other engineers to help build their technical competencies in fluid mechanics and multiphase modeling.

About You

Skills and Qualifications

Masters with thesis or PhD degree from a recognized university in petroleum/chemical/mechanical/ civil engineering disciplines with minimum GPA

Up to 3 years of research (academic/industry) experience in flow modeling and simulation in solving business problems.

Strong Fluid Mechanics and Computation Fluid Dynamics fundamentals and experience with Numerical Methods and Engineering Applications such as Ansys Fluent, STAR-CCM+, Open FOAM, Olga for solving Computational Fluid Dynamics or flow analysis problems

Preferred Qualifications

Familiarity with data manipulation and visualization in any programming language (preferably Python or MATLAB) for analyzing experimental, simulation or sensor data.

Experience in working with problems related to multiphase flow modeling is preferred.

Experience in data science (, machine learning, optimization) will be an added advantage.

Demonstrated teamwork, communication and leadership skills are essential.

Your benefits

An ExxonMobil career is one designed to last. Our commitment to you runs deep our employees grow personally and professionally, with benefits built on our core categories of health, security, finance and life. We offer you: 

Competitive compensation 

Medical plans, maternity leave and benefits, life, accidental death and dismemberment benefits 

Retirement benefits 

Global networking & cross-functional opportunities

Annual vacations & holidays

Day care assistance program

Training and development program

Tuition assistance program

Workplace flexibility policy

Relocation program

Transportation facility

Please note benefits may change from time to time without notice, subject to applicable laws. The benefits programs are based on the Company’s eligibility guidelines.

Stay connected with us

Learn more about ExxonMobil in India, visit and .

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EEO Statement

**Introduction**
At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.
**Your role and responsibilities**
We are looking for a talented and highly motivated engineer to help advance our effort on creating the most efficient large language models, with a specific focus on creating value for Enterprises. The candidate will be responsible for training/finetuning of language models, developing prototype solutions to real-world problems, working closely with IBM Infrastructure teams, and IBM scientists in a flexible and fun environment.
**Required technical and professional expertise**
* Experience with Python and Cloud
* Experience with Pytorch and FSDP
* Exposure to tuning and GPU optimization
IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

Research Engineer, Applied Research (Biotech AI – Drug Discovery)

About the Company

Quantiphi is an award-winning AI-first digital engineering company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.

Quantiphi has seen 2.5x growth YoY since its inception in 2013 to 3500+ team members globally.

For more details, please visit our website or LinkedIn page.

About the Applied Research Unit

Applied Research is an R&D practice at Quantiphi focused on advancing the frontiers of AI technologies with Applied Machine Learning at its core. We ideate and build novel solutions to high-impact, cutting-edge challenges, with a focus on advanced prototyping and scalable proof of concepts.

Within this unit, the AI-Accelerated Drug Discovery practice is a key pillar that aims to apply state-of-the-art AI methodologies to revolutionize the way new therapeutics are discovered and developed. We are committed to driving meaningful scientific breakthroughs by combining strong AI research with deep cross-disciplinary collaboration.

Job Description

Role Level: Research Engineer

Work Location: India

Resource Count: 2

The Role

This is a unique opportunity to work on scientifically impactful problems at the intersection of AI and biotechnology within Quantiphi Applied Research team. In this role, you will work on the development of core AI models and algorithms aimed at accelerating the drug discovery process. The position focuses on advancing foundational AI techniques such as generative modeling, optimization, and reinforcement learning, applied to molecular and bio-pharmaceutical data.

The position involves working with a diverse, lively, and proactive group of nerds who are constantly raising the bar on translating the latest AI research in Healthcare and Life Sciences into tangible reusable assets for the community. Hence this would require a high level of conceptual understanding, attention to detail and agility in terms of adaptation to new technologies.

While prior experience in the biotech or life sciences domain is highly valued and will elevate the candidate profile, we are equally open to exceptional AI/ML researchers from other domains who are excited to explore and learn the nuances of this rapidly growing field .

Please note: This is a core AI research role, not a software engineering or system integration position. We are particularly keen to engage with candidates focused on scientific AI innovation rather than application development or LLM/GenAI-centric workflows .

Responsibilities

• Stay ahead of the AI research curve, focusing on foundational AI methodologies applicable to drug discovery and molecular design.
• Build rapid prototypes, conduct detailed experimental studies, and develop advanced AI models in areas such as generative modeling, reinforcement learning, graph-based learning, and molecular property prediction.
• Work closely with interdisciplinary teams including biologists, chemists, and life science domain experts to design scientifically sound AI approaches.
• Contribute to Quantiphi IP portfolio through the development of novel algorithms, proof of concepts, and potential publications.
• Drive thought leadership through documentation, knowledge dissemination, and participation in conferences, blogs, webinars, and publications.
• Publish Research papers in prestigious Conferences and Journals

Requirements

Must Have:

• Master’s degree, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields.
• Minimum work experience required : from new graduates to 3+ yrs of research experience post graduation (in ML research)
• Strong foundation in AI/ML concepts with hands-on experience in model development, experimental design, and large-scale data analysis.
• Excellent in-depth understanding of ML concepts and the respective underlying mathematical know-how.
• Working knowledge of using NLP with biological sequences.
• Solid research mindset with a track record of working on complex AI problems—experience with drug discovery datasets is a plus but not a prerequisite.
• Excellent programming skills in Python, with experience using AI/ML frameworks like PyTorch or TensorFlow.
• Hands-on experience in developing and deploying models with various deep learning architectures in multiple ML areas like Computer-Vision, NLP, Statistics etc
• Ability to independently learn new scientific domains and apply AI techniques to novel bio-pharmaceutical problems.
• Strong communication skills with the ability to present complex ideas in an accessible format across audiences.
• Ability to translate abstract highlights into understandable insights in multiple knowledge-dissemination formats like blogs, presentations, paper-publications, tutorials and webinars

Good to Have:

• Prior exposure to molecular datasets, cheminformatics, bioinformatics, or life sciences.
• Hands-on experience with insilico techniques in drug discovery
• Hands-on experience with HPC workflows with genome datasets
• Familiarity with generative chemistry models, graph neural networks, reinforcement learning, or multi-objective optimization.
• Demonstrated industry research experience will be considered as an additional bonus.
• Research publications in AI/ML conferences such as NeurIPS, ICML, ICLR, or relevant bioinformatics journals
• Experience with cloud environments like GCP or AWS and scalable model training.
• Strong classical education on math/physics/mechanics/CS/Engineering concepts will also be an advantage.

Why Join Us?

• Opportunity to work at the cutting edge of AI and biotechnology, solving problems with real-world scientific impact.
• Exposure to interdisciplinary teams and a culture that encourages continuous learning and exploration.
• Contribute to an R&D environment that values curiosity, innovation, and the advancement of AI for good.

Research Engineer, Applied Research (Biotech AI – Drug Discovery)

About the Company
Quantiphi is an award-winning AI-first digital engineering company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.
Quantiphi has seen 2.5x growth YoY since its inception in 2013 to 3500+ team members globally.
For more details, please visit our website or LinkedIn page.

About the Applied Research Unit
Applied Research is an R&D practice at Quantiphi focused on advancing the frontiers of AI technologies with Applied Machine Learning at its core. We ideate and build novel solutions to high-impact, cutting-edge challenges, with a focus on advanced prototyping and scalable proof of concepts.
Within this unit, the AI-Accelerated Drug Discovery practice is a key pillar that aims to apply state-of-the-art AI methodologies to revolutionize the way new therapeutics are discovered and developed. We are committed to driving meaningful scientific breakthroughs by combining strong AI research with deep cross-disciplinary collaboration.

Job Description
Role Level: Research Engineer
Work Location: India
Resource Count: 2

The Role
This is a unique opportunity to work on scientifically impactful problems at the intersection of AI and biotechnology within Quantiphi Applied Research team. In this role, you will work on the development of core AI models and algorithms aimed at accelerating the drug discovery process. The position focuses on advancing foundational AI techniques such as generative modeling, optimization, and reinforcement learning, applied to molecular and bio-pharmaceutical data.
The position involves working with a diverse, lively, and proactive group of nerds who are constantly raising the bar on translating the latest AI research in Healthcare and Life Sciences into tangible reusable assets for the community. Hence this would require a high level of conceptual understanding, attention to detail and agility in terms of adaptation to new technologies.
While prior experience in the biotech or life sciences domain is highly valued and will elevate the candidate profile, we are equally open to exceptional AI/ML researchers from other domains who are excited to explore and learn the nuances of this rapidly growing field .
Please note: This is a core AI research role, not a software engineering or system integration position. We are particularly keen to engage with candidates focused on scientific AI innovation rather than application development or LLM/GenAI-centric workflows .
Responsibilities
Stay ahead of the AI research curve, focusing on foundational AI methodologies applicable to drug discovery and molecular design.
Build rapid prototypes, conduct detailed experimental studies, and develop advanced AI models in areas such as generative modeling, reinforcement learning, graph-based learning, and molecular property prediction.
Work closely with interdisciplinary teams including biologists, chemists, and life science domain experts to design scientifically sound AI approaches.
Contribute to Quantiphi IP portfolio through the development of novel algorithms, proof of concepts, and potential publications.
Drive thought leadership through documentation, knowledge dissemination, and participation in conferences, blogs, webinars, and publications.
Publish Research papers in prestigious Conferences and Journals
Requirements
Must Have:
Master’s degree, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields.
Minimum work experience required : from new graduates to 3+ yrs of research experience post graduation (in ML research)
Strong foundation in AI/ML concepts with hands-on experience in model development, experimental design, and large-scale data analysis.
Excellent in-depth understanding of ML concepts and the respective underlying mathematical know-how.
Working knowledge of using NLP with biological sequences.
Solid research mindset with a track record of working on complex AI problems—experience with drug discovery datasets is a plus but not a prerequisite.
Excellent programming skills in Python, with experience using AI/ML frameworks like PyTorch or TensorFlow.
Hands-on experience in developing and deploying models with various deep learning architectures in multiple ML areas like Computer-Vision, NLP, Statistics etc
Ability to independently learn new scientific domains and apply AI techniques to novel bio-pharmaceutical problems.
Strong communication skills with the ability to present complex ideas in an accessible format across audiences.
Ability to translate abstract highlights into understandable insights in multiple knowledge-dissemination formats like blogs, presentations, paper-publications, tutorials and webinars
Good to Have:
Prior exposure to molecular datasets, cheminformatics, bioinformatics, or life sciences.
Hands-on experience with insilico techniques in drug discovery
Hands-on experience with HPC workflows with genome datasets
Familiarity with generative chemistry models, graph neural networks, reinforcement learning, or multi-objective optimization.
Demonstrated industry research experience will be considered as an additional bonus.
Research publications in AI/ML conferences such as NeurIPS, ICML, ICLR, or relevant bioinformatics journals
Experience with cloud environments like GCP or AWS and scalable model training.
Strong classical education on math/physics/mechanics/CS/Engineering concepts will also be an advantage.
Why Join Us?
Opportunity to work at the cutting edge of AI and biotechnology, solving problems with real-world scientific impact.
Exposure to interdisciplinary teams and a culture that encourages continuous learning and exploration.
Contribute to an R&D environment that values curiosity, innovation, and the advancement of AI for good.

Research Engineer, Applied Research (Biotech AI – Drug Discovery)

About the Company

Quantiphi is an award-winning AI-first digital engineering company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.

Quantiphi has seen 2.5x growth YoY since its inception in 2013 to 3500+ team members globally.

For more details, please visit our website or page.

About the Applied Research Unit

Applied Research is an R&D practice at Quantiphi focused on advancing the frontiers of AI technologies with Applied Machine Learning at its core. We ideate and build novel solutions to high-impact, cutting-edge challenges, with a focus on advanced prototyping and scalable proof of concepts.

Within this unit, the AI-Accelerated Drug Discovery practice is a key pillar that aims to apply state-of-the-art AI methodologies to revolutionize the way new therapeutics are discovered and developed. We are committed to driving meaningful scientific breakthroughs by combining strong AI research with deep cross-disciplinary collaboration.

Job Description

Role Level: Research Engineer

Work Location: India

Resource Count: 2

The Role

This is a unique opportunity to work on scientifically impactful problems at the intersection of AI and biotechnology within Quantiphi Applied Research team. In this role, you will work on the development of core AI models and algorithms aimed at accelerating the drug discovery process. The position focuses on advancing foundational AI techniques such as generative modeling, optimization, and reinforcement learning, applied to molecular and bio-pharmaceutical data.

The position involves working with a diverse, lively, and proactive group of nerds who are constantly raising the bar on translating the latest AI research in Healthcare and Life Sciences into tangible reusable assets for the community. Hence this would require a high level of conceptual understanding, attention to detail and agility in terms of adaptation to new technologies.

While prior experience in the biotech or life sciences domain is highly valued and will elevate the candidate profile, we are equally open to exceptional AI/ML researchers from other domains who are excited to explore and learn the nuances of this rapidly growing field .

Please note: This is a core AI research role, not a software engineering or system integration position. We are particularly keen to engage with candidates focused on scientific AI innovation rather than application development or LLM/GenAI-centric workflows .

Responsibilities

• Stay ahead of the AI research curve, focusing on foundational AI methodologies applicable to drug discovery and molecular design.
• Build rapid prototypes, conduct detailed experimental studies, and develop advanced AI models in areas such as generative modeling, reinforcement learning, graph-based learning, and molecular property prediction.
• Work closely with interdisciplinary teams including biologists, chemists, and life science domain experts to design scientifically sound AI approaches.
• Contribute to Quantiphi IP portfolio through the development of novel algorithms, proof of concepts, and potential publications.
• Drive thought leadership through documentation, knowledge dissemination, and participation in conferences, blogs, webinars, and publications.
• Publish Research papers in prestigious Conferences and Journals

Requirements

Must Have:

• Master’s degree, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields.
• Minimum work experience required : from new graduates to 3+ yrs of research experience post graduation (in ML research)
• Strong foundation in AI/ML concepts with hands-on experience in model development, experimental design, and large-scale data analysis.
• Excellent in-depth understanding of ML concepts and the respective underlying mathematical know-how.
• Working knowledge of using NLP with biological sequences.
• Solid research mindset with a track record of working on complex AI problems—experience with drug discovery datasets is a plus but not a prerequisite.
• Excellent programming skills in Python, with experience using AI/ML frameworks like PyTorch or TensorFlow.
• Hands-on experience in developing and deploying models with various deep learning architectures in multiple ML areas like Computer-Vision, NLP, Statistics etc
• Ability to independently learn new scientific domains and apply AI techniques to novel bio-pharmaceutical problems.
• Strong communication skills with the ability to present complex ideas in an accessible format across audiences.
• Ability to translate abstract highlights into understandable insights in multiple knowledge-dissemination formats like blogs, presentations, paper-publications, tutorials and webinars

Good to Have:

• Prior exposure to molecular datasets, cheminformatics, bioinformatics, or life sciences.
• Hands-on experience with insilico techniques in drug discovery
• Hands-on experience with HPC workflows with genome datasets
• Familiarity with generative chemistry models, graph neural networks, reinforcement learning, or multi-objective optimization.
• Demonstrated industry research experience will be considered as an additional bonus.
• Research publications in AI/ML conferences such as NeurIPS, ICML, ICLR, or relevant bioinformatics journals
• Experience with cloud environments like GCP or AWS and scalable model training.
• Strong classical education on math/physics/mechanics/CS/Engineering concepts will also be an advantage.

Why Join Us?

• Opportunity to work at the cutting edge of AI and biotechnology, solving problems with real-world scientific impact.
• Exposure to interdisciplinary teams and a culture that encourages continuous learning and exploration.
• Contribute to an R&D environment that values curiosity, innovation, and the advancement of AI for good.

About QpiAI

At QpiAI, we are leading the effort to discover optimal AI and Quantum systems in Life sciences,

Healthcare, Transportation, Finance, Industrial, and Space technologies. QpiAI is building a full-

stack Enterprise Quantum Computers.

QpiAI Quantum hardware team is responsible for designing and characterisation of Quantum

Processor, Cryogenic Quantum Control Circuits, RF Control Hardware, and QpiAI ASGP.

About the Role

We are building high-performance optimization infrastructure for real-world decision-making problems across logistics, manufacturing, and emerging tech domains. We are looking for a mathematically inclined, algorithmically sharp engineer who thrives at the intersection of theory and systems — someone who can translate abstract optimization problems into efficient, production-ready solvers.

Key Responsibilities

• Design and implement fast, scalable solvers for complex optimization problems across discrete and continuous domains.
• Develop constraint modeling frameworks and metaheuristic algorithms grounded in strong mathematical principles.
• Evaluate solution quality, convergence behavior, and performance benchmarks across diverse instances and datasets.
• Work with system engineers to integrate your solver modules into larger optimization stacks and real-time decision systems.
• Explore and adapt techniques from mathematical programming, stochastic methods, and quantum-inspired approaches.

What We're Looking For

• Strong foundation in mathematics : linear algebra, combinatorics, graph theory, numerical methods, convex and discrete optimization.
• Algorithmic and systems thinking : should be able to write fast, memory-efficient code and optimize for performance bottlenecks.
• Exceptional programming skills in Python and C++ (or Rust/Julia); low-level optimizations and profiler-driven development are a plus.
• Experience with algorithm design for constraint systems, heuristics, and metaheuristics.
• Hands-on coding profile : A high rating on platforms like Leetcode, Codeforces, or Hackerrank is a strong signal.
• Product thinking : capable of modular, reusable, and extensible solver design; understands how solvers scale in production systems.
• Exposure to quantum computing or hybrid quantum-classical paradigms is a plus.

Good to Have

• Familiarity with model encoding techniques and constraint representations.
• Benchmarking experience on large-scale combinatorial datasets.
• Participation in mathematical modeling competitions (e.g., INMO, COMAP, Kaggle competitions involving optimization).

Why Join Us

You’ll work closely with a team that understands optimization not just as a mathematical challenge, but as a product engineering problem. If you're excited about building fast solvers, pushing boundaries in hybrid or hardware-accelerated optimization, and solving problems at scale, we’d love to hear from you.

About QpiAI
At QpiAI, we are leading the effort to discover optimal AI and Quantum systems in Life sciences,
Healthcare, Transportation, Finance, Industrial, and Space technologies. QpiAI is building a full-
stack Enterprise Quantum Computers.
QpiAI Quantum hardware team is responsible for designing and characterisation of Quantum
Processor, Cryogenic Quantum Control Circuits, RF Control Hardware, and QpiAI ASGP.

About the Role
We are building high-performance optimization infrastructure for real-world decision-making problems across logistics, manufacturing, and emerging tech domains. We are looking for a mathematically inclined, algorithmically sharp engineer who thrives at the intersection of theory and systems — someone who can translate abstract optimization problems into efficient, production-ready solvers.

Key Responsibilities
Design and implement fast, scalable solvers for complex optimization problems across discrete and continuous domains.
Develop constraint modeling frameworks and metaheuristic algorithms grounded in strong mathematical principles.
Evaluate solution quality, convergence behavior, and performance benchmarks across diverse instances and datasets.
Work with system engineers to integrate your solver modules into larger optimization stacks and real-time decision systems.
Explore and adapt techniques from mathematical programming, stochastic methods, and quantum-inspired approaches.

What We're Looking For
Strong foundation in mathematics : linear algebra, combinatorics, graph theory, numerical methods, convex and discrete optimization.
Algorithmic and systems thinking : should be able to write fast, memory-efficient code and optimize for performance bottlenecks.
Exceptional programming skills in Python and C++ (or Rust/Julia); low-level optimizations and profiler-driven development are a plus.
Experience with algorithm design for constraint systems, heuristics, and metaheuristics.
Hands-on coding profile : A high rating on platforms like Leetcode, Codeforces, or Hackerrank is a strong signal.
Product thinking : capable of modular, reusable, and extensible solver design; understands how solvers scale in production systems.
Exposure to quantum computing or hybrid quantum-classical paradigms is a plus.

Good to Have
Familiarity with model encoding techniques and constraint representations.
Benchmarking experience on large-scale combinatorial datasets.
Participation in mathematical modeling competitions (e.g., INMO, COMAP, Kaggle competitions involving optimization).

Why Join Us
You’ll work closely with a team that understands optimization not just as a mathematical challenge, but as a product engineering problem. If you're excited about building fast solvers, pushing boundaries in hybrid or hardware-accelerated optimization, and solving problems at scale, we’d love to hear from you.

About QpiAI

At QpiAI, we are leading the effort to discover optimal AI and Quantum systems in Life sciences,

Healthcare, Transportation, Finance, Industrial, and Space technologies. QpiAI is building a full-

stack Enterprise Quantum Computers.

QpiAI Quantum hardware team is responsible for designing and characterisation of Quantum

Processor, Cryogenic Quantum Control Circuits, RF Control Hardware, and QpiAI ASGP.

About the Role

We are building high-performance optimization infrastructure for real-world decision-making problems across logistics, manufacturing, and emerging tech domains. We are looking for a mathematically inclined, algorithmically sharp engineer who thrives at the intersection of theory and systems — someone who can translate abstract optimization problems into efficient, production-ready solvers.

Key Responsibilities

• Design and implement fast, scalable solvers for complex optimization problems across discrete and continuous domains.
• Develop constraint modeling frameworks and metaheuristic algorithms grounded in strong mathematical principles.
• Evaluate solution quality, convergence behavior, and performance benchmarks across diverse instances and datasets.
• Work with system engineers to integrate your solver modules into larger optimization stacks and real-time decision systems.
• Explore and adapt techniques from mathematical programming, stochastic methods, and quantum-inspired approaches.

What We're Looking For

• Strong foundation in mathematics : linear algebra, combinatorics, graph theory, numerical methods, convex and discrete optimization.
• Algorithmic and systems thinking : should be able to write fast, memory-efficient code and optimize for performance bottlenecks.
• Exceptional programming skills in Python and C++ (or Rust/Julia); low-level optimizations and profiler-driven development are a plus.
• Experience with algorithm design for constraint systems, heuristics, and metaheuristics.
• Hands-on coding profile : A high rating on platforms like Leetcode, Codeforces, or Hackerrank is a strong signal.
• Product thinking : capable of modular, reusable, and extensible solver design; understands how solvers scale in production systems.
• Exposure to quantum computing or hybrid quantum-classical paradigms is a plus.

Good to Have

• Familiarity with model encoding techniques and constraint representations.
• Benchmarking experience on large-scale combinatorial datasets.
• Participation in mathematical modeling competitions (e.g., INMO, COMAP, Kaggle competitions involving optimization).

Why Join Us

You’ll work closely with a team that understands optimization not just as a mathematical challenge, but as a product engineering problem. If you're excited about building fast solvers, pushing boundaries in hybrid or hardware-accelerated optimization, and solving problems at scale, we’d love to hear from you.