387 Drug Discovery jobs in India

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.

Employment Type: Full-Time/Remote

The Role:

We’re seeking a hands-on AI/ML engineer to lead the architecture and implementation of our next-generation discovery pipeline. This role involves building tools that link disease biology with structure-based design, generative modeling, and safety prediction — all wrapped in a highly interoperable, graph-native infrastructure.

You’ll be central to defining and building the intelligence behind our system: from data ingestion to model inference, and from semantic reasoning to compound evaluation.

Key Responsibilities:

• Architect and implement AI-driven pipelines for multi-stage drug discovery
• Develop and integrate predictive modeling tools for structural and chemical analysis
• Work with generative algorithms to explore and optimize chemical space
• Connect computational models to a multi-domain biomedical knowledge graph (RDF/OWL)
• Design scoring and validation routines to assess novelty, fit, and biological plausibility
• Build scalable, containerized workflows using modern MLops and data engineering tools
• Collaborate with experts in biology, chemistry, and knowledge engineering to translate real-world constraints into robust, modular software

Required Qualifications:

• Strong programming skills in Python and experience with scientific computing stacks
• 2+ years of hands-on experience in machine learning, deep learning, or graph-based modeling
• Experience building or working with biomedical or chemical data pipelines
• Familiarity with cheminformatics and structural biology data (e.g., proteins, small molecules)
• Practical experience with model evaluation, explainability, and data provenance

Nice to Have:

• Experience with generative modeling (e.g., sequence-based, graph-based, or diffusion models)
• Familiarity with multi-objective optimization or reinforcement learning in scientific domains
• Exposure to open-source tools in bio/cheminformatics, or contributions to research software
• Background in systems biology, pharmacology, or drug development is a plus

Why Join Us?

• Help build a drug discovery platform that’s explainable, reproducible, and open to the world
• Work at the intersection of AI, biology, and semantic data systems
• Join a collaborative and mission-driven team of scientists and engineers
• Influence not just tools, but how the future of therapeutics is discovered

·   About Us:

Pattern Agentix is pioneering a new model for drug discovery — open, explainable, and grounded in biological reality. We’re developing a fully modular AI platform that integrates predictive modeling, generative design, and a richly structured biomedical knowledge graph. Our goal is to build an alternative to black-box pharma pipelines, driven by transparency, biological context, and cutting-edge machine learning.