Computational Biology & ML Engineer – Peptide & Protein and chemical Design

Location: India (Remote/Hybrid)

Engagement: Full time long-term contract

We are building an AI-enabled platform for designing and evaluating small molecules, peptide and folded protein binders. We are looking for a senior, hands-on individual contributor who can serve as both the technical lead and scientific subject matter expert for this initiative.

This is not a purely academic or software-engineering role. The successful candidate must be able to design the scientific approach, Use AI to output production-quality code, test the complete workflow, and evaluate whether the results are biologically meaningful.

Key responsibilities

Build and validate end-to-end peptide and protein-design pipelines, from target preparation and candidate generation through structural verification and ranking.

Integrate tools such as BoltzGen, Boltz-2, Protenix, Reinvent 4,P2Rank, inverse-folding models, and protein-interface analysis methods.

Develop Python-based services for sequence generation, filtering, diversity selection, GPU-based inference, data capture, and reporting.

Design, train, and benchmark target-conditioned ML models using distillation, uncertainty estimation, applicability checks, and active-learning methods.





Establish scientific and software testing, including hard negatives, held-out target families, false-negative controls, reproducibility, throughput, and GPU-cost benchmarks.

Act as the primary scientific advisor to product, engineering, and experimental teams.

Required background

Advanced degree or equivalent experience in computational biology, bioinformatics, structural biology, machine learning, or a related field. Muster is a must.

At least 2 years of organizational work experience is a must.

Strong knowledge of small chemical molecules, peptide design, protein structure, protein-protein interfaces, and computational protein design.

Python and practical experience with PyTorch or JAX.

Experience working with Linux, Docker, GPUs, scientific data pipelines, APIs, and reproducible ML workflows.

Ability to interpret structural confidence, PAE, binding-site coverage, contacts, clashes, developability, and candidate diversity.

Demonstrated ability to independently build, test, and deliver scientific software.

Experience with BoltzGen/Boltz-2, AlphaFold-class models, Protenix, ProteinMPNN or other inverse-folding tools, PRODIGY, HPC/cloud GPU environments, and MLOps is highly desirable.

📌 Computational Biology & ML Engineer (India)
🏢 Pattern Agentix
📍 India