Machine Learning Engineer(Pharma/Biotech Background)

Location: Remote Part Time: 10-18 hrs/week.

Duration: 12+ months contract

Pay Rate: $50-$60/hr on W2

Key Responsibilities: Bridge the gap between ML and Data Infrastructure by building robust interim data pipelines (Python, Pandas, SQL) to distill complex, unstructured datasets into ML-ready formats, and partnering with Data Engineering to co-design scalable, long-term database workflows. Drive end-to-end pilot modeling by training, tuning, and delivering robust statistical baseline models and advanced graph-based prototypes across four distinct biological pilot projects. Translate architectural vision into reality by iterating rapidly based on ML architectural guidance, turning theoretical concepts into functional, high-impact proofs-of-concept within 3-4 months. Translate complex code into accessible tools by packaging and structuring the ML codebase so that Biological Domain Owners can independently execute model runs, perform standard evaluations, and validate hypotheses.

Education & Experience: Advanced degree (M.S. or Ph. D.) in Computer Science, Bioinformatics, or related field is preferred, alongside 3+ years of hands-on Machine Learning Engineering experience. Equivalent practical experience will be strongly considered in lieu of an advanced degree, provided the candidate has a demonstrated track record (5+ years) of building and deploying end-to-end ML pipelines in dynamic environments. Deep Technical Expertise: Proven experience developing and deploying applied machine learning baselines (e.g., Scikit-learn).High proficiency in Python, Pandas, and SQL for agile data engineering and scripting. Experience with advanced deep learning frameworks (PyTorch, Torch Geometric) or agentic systems is a strong plus. Strong understanding of how to structure complex data to natively support seamless ML modeling. Translation & Execution Experience Proven ability to translate abstract biological/business problems and unstructured data into concrete ML formulations. Conversely, you know how to translate complex ML outputs and codebases into user-friendly tools for non-ML experts (biologists and domain owners).You possess a strong "builder's mindset" and apply the 80/20 rule effectively—prioritizing rapid, high-impact proofs-of-concept over slow perfection to unlock immediate value in drug discovery.