424 Deep Learning jobs in Hyderabad

NVIDIA is known as “the AI computing company.” Come, join our Deep Learning team, where you can help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field. We are currently seeking an experienced senior software engineer with strong Deep Learning and System Programming fundamentals coupled with robust C/C++ skills to contribute to the development of NVIDIA Maxine Audio - a comprehensive suite of SDKs, Microservices and APIs that enable AI-driven features for video conferencing, content creation and gaming.

What you’ll be doing:

• Architect and lead development of next-generation Deep Learning and multimedia algorithms for processing of speech and audio applications.

• Train Speech Enhancement models, assess them for quality, performance, and finetune them.

• Analyze model accuracy and bias and recommend the next course of action & Improvements.

• Improve processes for speech data processing, augmentation, filtering & training sets preparation.

• Optimize algorithms for optimal performance on the GPU tensor cores

• Collaborate with various teams to drive an end to end workflow from data curation and training to performance optimization and deployment

• Influence strategic decisions in the team and product roadmap

• Partner with system software engineers and validation teams to build and ship production-quality code.

What we need to see:

• PH.D./MS in Computer Science or a closely related engineering field with 3+ years of relevant experience

• Strong background in Deep Learning including model design, pruning & performance optimization, transfer learning etc

• 4+ years of experience of leading cross-module projects and taking them to productization

• Strong software engineering background with proficiency in C or C++

• Hands-on expertise with PyTorch, TensorRT, CuDNN and one or more Deep Learning frameworks (Tensorflow, Keras etc)

• Familiarity/expertize with various cloud frameworks e.g. AWS, GCP, Azure is a big plus

• CUDA programming experience is a plus

• Excellent communication and collaboration skills

• Self-motivated and able to find creative practical solutions to problems

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Pattern is developing a cutting-edge AI drug discovery engine that combines AlphaFold/OpenFold structural predictions, generative molecular design, and reinforcement learning agents to navigate the ~10^60 possibilities in small-molecule chemical space.

We are seeking a Deep Learning Lead to architect, train, and deploy machine learning models for protein–ligand structure prediction, de novo molecular generation, and multi-objective optimization. You will partner closely with our Ligand Design & Pose Prediction Lead to integrate chemical and biological expertise into the model pipeline, ensuring our AI agents produce compounds that are potent, novel, and biologically relevant.

Key Responsibilities

Design and implement deep learning architectures for:

Pocket-conditioned molecular generation (e.g., Pocket2Mol, SE(3)-equivariant GNNs, 3D diffusion models).

Protein–ligand pose prediction (DiffDock, EquiBind, custom SE(3)-transformers).

Multi-objective reinforcement learning for compound optimization (potency, ADMET, novelty).

Fine-tune AlphaFold/OpenFold and related structure models for project-specific targets.

Integrate multi-modal biological context (from the Agentix Knowledge Graph) into generative and scoring models.

Develop and maintain scoring functions for binding affinity, selectivity, ADMET, and synthetic accessibility.

Implement uncertainty estimation and active learning loops to prioritize compound synthesis/testing.

Collaborate with the Ligand Design Lead to:

Translate medicinal chemistry insights into model constraints.

Incorporate wet-lab feedback into model retraining.

Co-develop workflows for real-time human–AI co-design.

Maintain MLOps pipelines for dataset versioning, model deployment, and experiment tracking.

Qualifications Required

PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.

2+ years of hands-on experience in deep learning model development, ideally in a scientific or molecular domain.

Strong expertise in generative modeling (transformers, diffusion models, graph neural networks).

Experience with 3D geometric deep learning for molecular structures (SE(3)-equivariant architectures).

Proficiency in reinforcement learning (policy gradients, model-based RL, quality-diversity search).

Strong programming skills in Python with frameworks like PyTorch or TensorFlow.

Experience in handling molecular datasets (PDB, ChEMBL, binding affinity data).

Preferred

Prior work on protein structure prediction, ligand docking, or molecular property prediction.

Familiarity with cheminformatics toolkits (RDKit, Open Babel).

Experience in integrating wet-lab assay data into active learning loops.

MLOps experience (Docker, CI/CD for ML, MLflow, DVC).

Why Join Us?

You’ll help define the AI engine at the heart of Pattern’s platform, working with cutting-edge molecular AI tools and direct structural chemistry input from our Ligand Design Lead. Your models will power a proprietary agentic search system designed to explore the chemical universe faster and smarter than competitors.

Pattern is developing a cutting-edge AI drug discovery engine that combines AlphaFold/OpenFold structural predictions, generative molecular design, and reinforcement learning agents to navigate the ~10^60 possibilities in small-molecule chemical space.

We are seeking a Deep Learning Lead to architect, train, and deploy machine learning models for protein–ligand structure prediction, de novo molecular generation, and multi-objective optimization. You will partner closely with our Ligand Design & Pose Prediction Lead to integrate chemical and biological expertise into the model pipeline, ensuring our AI agents produce compounds that are potent, novel, and biologically relevant.

Key Responsibilities

Design and implement deep learning architectures for:

Pocket-conditioned molecular generation (e.g., Pocket2Mol, SE(3)-equivariant GNNs, 3D diffusion models).

Protein–ligand pose prediction (DiffDock, EquiBind, custom SE(3)-transformers).

Multi-objective reinforcement learning for compound optimization (potency, ADMET, novelty).

Fine-tune AlphaFold/OpenFold and related structure models for project-specific targets.

Integrate multi-modal biological context (from the Agentix Knowledge Graph) into generative and scoring models.

Develop and maintain scoring functions for binding affinity, selectivity, ADMET, and synthetic accessibility.

Implement uncertainty estimation and active learning loops to prioritize compound synthesis/testing.

Collaborate with the Ligand Design Lead to:

Translate medicinal chemistry insights into model constraints.

Incorporate wet-lab feedback into model retraining.

Co-develop workflows for real-time human–AI co-design.

Maintain MLOps pipelines for dataset versioning, model deployment, and experiment tracking.

Qualifications Required

PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.

2+ years of hands-on experience in deep learning model development, ideally in a scientific or molecular domain.

Strong expertise in generative modeling (transformers, diffusion models, graph neural networks).

Experience with 3D geometric deep learning for molecular structures (SE(3)-equivariant architectures).

Proficiency in reinforcement learning (policy gradients, model-based RL, quality-diversity search).

Strong programming skills in Python with frameworks like PyTorch or TensorFlow.

Experience in handling molecular datasets (PDB, ChEMBL, binding affinity data).

Preferred

Prior work on protein structure prediction, ligand docking, or molecular property prediction.

Familiarity with cheminformatics toolkits (RDKit, Open Babel).

Experience in integrating wet-lab assay data into active learning loops.

MLOps experience (Docker, CI/CD for ML, MLflow, DVC).

Why Join Us?

You’ll help define the AI engine at the heart of Pattern’s platform, working with cutting-edge molecular AI tools and direct structural chemistry input from our Ligand Design Lead. Your models will power a proprietary agentic search system designed to explore the chemical universe faster and smarter than competitors.

Pattern is developing a cutting-edge AI drug discovery engine that combines AlphaFold/OpenFold structural predictions, generative molecular design, and reinforcement learning agents to navigate the ~10^60 possibilities in small-molecule chemical space.

We are seeking a Deep Learning Lead to architect, train, and deploy machine learning models for protein–ligand structure prediction, de novo molecular generation, and multi-objective optimization. You will partner closely with our Ligand Design & Pose Prediction Lead to integrate chemical and biological expertise into the model pipeline, ensuring our AI agents produce compounds that are potent, novel, and biologically relevant.

Key Responsibilities

Design and implement deep learning architectures for:

Pocket-conditioned molecular generation (e.g., Pocket2Mol, SE(3)-equivariant GNNs, 3D diffusion models).

Protein–ligand pose prediction (DiffDock, EquiBind, custom SE(3)-transformers).

Multi-objective reinforcement learning for compound optimization (potency, ADMET, novelty).

Fine-tune AlphaFold/OpenFold and related structure models for project-specific targets.

Integrate multi-modal biological context (from the Agentix Knowledge Graph) into generative and scoring models.

Develop and maintain scoring functions for binding affinity, selectivity, ADMET, and synthetic accessibility.

Implement uncertainty estimation and active learning loops to prioritize compound synthesis/testing.

Collaborate with the Ligand Design Lead to:

Translate medicinal chemistry insights into model constraints.

Incorporate wet-lab feedback into model retraining.

Co-develop workflows for real-time human–AI co-design.

Maintain MLOps pipelines for dataset versioning, model deployment, and experiment tracking.

Qualifications Required

PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.

2+ years of hands-on experience in deep learning model development, ideally in a scientific or molecular domain.

Strong expertise in generative modeling (transformers, diffusion models, graph neural networks).

Experience with 3D geometric deep learning for molecular structures (SE(3)-equivariant architectures).

Proficiency in reinforcement learning (policy gradients, model-based RL, quality-diversity search).

Strong programming skills in Python with frameworks like PyTorch or TensorFlow.

Experience in handling molecular datasets (PDB, ChEMBL, binding affinity data).

Preferred

Prior work on protein structure prediction, ligand docking, or molecular property prediction.

Familiarity with cheminformatics toolkits (RDKit, Open Babel).

Experience in integrating wet-lab assay data into active learning loops.

MLOps experience (Docker, CI/CD for ML, MLflow, DVC).

Why Join Us?

You’ll help define the AI engine at the heart of Pattern’s platform, working with cutting-edge molecular AI tools and direct structural chemistry input from our Ligand Design Lead. Your models will power a proprietary agentic search system designed to explore the chemical universe faster and smarter than competitors.