Machine Learning Engineer(Pharma/Biotech Background)

Title: Machine Learning Engineer
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.
Who You Are 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.

Work arrangement
Yes