Our client is a leading global life sciences organization leveraging AI, machine learning, data science, and computational modeling to accelerate drug discovery and development.
We are seeking a Machine Learning Engineer to join an AI for Drug Discovery team focused on applying machine learning to large-molecule and antibody discovery. This is a highly collaborative, hands-on role supporting multiple active biological pilot projects.
Title: Machine Learning Engineer - Functional Modeling (Part-time) Job Type: Part-Time W2 Contract
Duration: 12 Months (Likely to extend)
Hours: 16 hours/week
Location: Remote within the U.S. or Canada
Position Overview The ideal candidate is a builder who can quickly translate computational concepts into working ML solutions, while bridging the gap between machine learning, data engineering, and biological research teams.
This role will have immediate impact by developing ML baselines, building interim data pipelines, creating evaluation tools, and helping establish scalable foundations for longer-term functional modeling initiatives.
Key R
esponsibilities - Build robust interim data pipelines using Python, Pandas, and SQL, transforming complex and unstructured datasets into ML-ready formats.
- Partner with Data Engineering teams to help design scalable, long-term database and data infrastructure workflows.
- Develop, train, tune, and evaluate machine learning baseline models across multiple biological pilot projects.
- Build advanced graph-based ML prototypes to support functional modeling and drug discovery applications.
- Translate ML architecture and computational concepts into functional proofs-of-concept within an accelerated 3-4 month timeframe.
- Rapidly iterate on models and workflows based on guidance from ML architects and feedback from scientific stakeholders.
- Package and structure ML codebases into accessible, reusable tools that enable biologists and other domain experts to run models and perform standard evaluations independently.
- Translate complex biological questions and unstructured datasets into practical machine learning problems and solutions.
- Interpret ML results and communicate technical concepts to non-ML experts, including biologists and scientific domain owners.
- Collaborate closely with ML architects, data engineers, computational scientists, and bench scientists to keep projects moving forward.
- Apply a pragmatic 80/20, proof-of-concept mindset, prioritizing rapid delivery and measurable impact over unnecessary complexity.
Required Qualifications - 3+ years of hands-on Machine Learning Engineering experience with an advanced degree (M.S. or Ph.D.) in Computer Science, Bioinformatics, Computational Biology, or a related field.
- 5+ years of practical experience building and deploying end-to-end ML pipelines may be considered in lieu of an advanced degree.
- Must be authorized to work in the United States without requiring immediate or future visa sponsorship.
- Must be able to work on a W-2 basis.
- Strong hands-on experience developing applied machine learning models and statistical baselines, particularly using Scikit-learn.
- Advanced proficiency in:
- Strong understanding of data preparation, data structures, and ML-ready data pipelines.
- Demonstrated ability to translate ambiguous scientific, biological, or business problems into concrete ML formulations.
- Experience building practical proofs-of-concept and production-oriented ML workflows.
- Strong communication skills and the ability to work effectively with both technical and scientific stakeholders.
Preferred Qualifications - Experience with PyTorch.
- Experience with PyTorch Geometric / graph neural networks.
- Experience applying ML to biological, pharmaceutical, biotech, or drug discovery problems.
- Experience working with large-molecule, antibody, protein, genomic, or other biological datasets.
- Experience with agentic AI systems.
- Experience developing tools or workflows that allow non-ML users to execute models and evaluate results.
- Experience collaborating across machine learning, data engineering, computational science, and laboratory/experimental teams.
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