Machine Learning Engineer (Part-Time Contract)

Job Summary

We are seeking a hands-on Machine Learning Engineer to build ML-ready data pipelines, develop statistical baseline models, and prototype graph-based machine learning solutions for biological research and drug discovery. The ideal candidate combines strong Python and data engineering skills with practical ML implementation experience and the ability to turn complex models into accessible tools for biological domain experts.

Key Responsibilities

• Data Pipelines: Build interim data pipelines using Python, Pandas, and SQL to transform complex, unstructured datasets into ML-ready formats.
• Machine Learning Development: Train, tune, evaluate, and deliver statistical baseline models using Scikit-learn and develop advanced graph-based ML prototypes across four biological pilot projects.
• Rapid Prototyping: Translate ML architecture guidance into functional proofs of concept, iterating quickly to deliver results within 3–4 months.
• Codebase & Tooling: Structure and package ML code so biological domain owners can independently execute model runs, evaluate results, and validate hypotheses.
• Data Infrastructure Collaboration: Partner with Data Engineering to design scalable, long-term database and data processing workflows.
• Problem Translation: Convert biological research questions and unstructured data into practical ML formulations and communicate model outputs to non-ML experts.

Required Qualifications

• 3+ years of hands-on Machine Learning Engineering experience with an M.S. or Ph.D. in Computer Science, Bioinformatics, or a related field preferred.
• Candidates with equivalent practical experience and 5+ years of building and deploying end-to-end ML pipelines will also be considered.
• Strong proficiency in Python, Pandas, and SQL for data processing, transformation, and pipeline development.
• Proven experience developing and evaluating applied ML models and statistical baselines using Scikit-learn.
• Experience building end-to-end ML pipelines and preparing complex datasets for modeling.
• Ability to translate abstract business or biological problems into concrete ML solutions.
• Strong problem-solving skills and a hands-on, delivery-focused mindset.

Preferred Qualifications

• Experience with PyTorch, PyTorch Geometric, Graph Neural Networks (GNNs), or other graph-based ML techniques.
• Exposure to bioinformatics, biological datasets, biomedical research, or drug discovery.
• Experience with agentic AI systems.
• Ability to package ML workflows into reusable tools for researchers and domain experts.

Contract Details

• Work Arrangement: Fully Remote
• Employment Type: Part-Time Contract
• Project Timeline: 3–4 Months
• Focus: Biological ML pilots, rapid prototyping, and ML-ready data infrastructure