Machine Learning Engineer
Develop and deploy the AI/ML models behind our life sciences and drug discovery intelligence platforms - biomedical data pipelines, molecular and property prediction, biomedical NLP, LLM and agent workflows, and the production services that serve them.
IndiaFull-time2–5 years
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Role Overview
We are looking for a Machine Learning Engineer to develop and deploy the AI/ML models powering our life sciences and drug discovery intelligence platforms.
You will work closely with our scientific and engineering teams to build practical ML solutions across biomedical data, drug discovery, scientific intelligence, NLP and AI agents.
Key Responsibilities
- Design, develop, train and evaluate machine learning models.
- Build ML pipelines for biomedical and life sciences datasets.
- Develop models for drug discovery, molecular and property prediction, classification, ranking and scientific intelligence.
- Work with LLMs, embeddings, RAG and AI-agent workflows where relevant.
- Process and integrate structured and unstructured biomedical data.
- Develop APIs and production-ready ML services.
- Evaluate model performance, reliability and scalability.
- Collaborate with the computational biology, drug discovery and software engineering teams.
Required Qualifications
- Master's degree or PhD in Computer Science, AI/ML, Data Science, Computational Biology, Bioinformatics or a related field.
- 2–5 years of hands-on machine learning experience.
- Strong Python programming skills.
- Experience with PyTorch, TensorFlow or equivalent ML frameworks.
- Strong understanding of supervised and unsupervised learning, and of model evaluation.
- Experience with data preprocessing, feature engineering and ML pipelines.
- Strong problem-solving and analytical skills.
Preferred Qualifications & Experience
Preferred Qualifications & Experience
Experience in one or more of the following:
- Drug discovery or computational biology.
- Bioinformatics or biomedical data.
- Molecular property prediction.
- QSAR and cheminformatics.
- Graph neural networks.
- NLP, and biomedical NLP in particular.
- Knowledge graphs.
- RDKit, Biopython or similar scientific libraries.
- FastAPI, PostgreSQL and cloud platforms.
Candidates with experience applying ML to pharma, biotechnology or healthcare will be strongly preferred.
Compensation
Depending on experience, technical capability and relevant domain expertise. Performance-based incentives and opportunities for growth within the technical team may be available.
How to Apply
- An updated CV or resume.
- GitHub, portfolio or relevant project links, if available.
- A brief description of one ML project you have developed or deployed, including the problem, your approach and the outcome.
Candidates with strong practical projects are encouraged to apply even if their academic background is not directly in life sciences.
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