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Machine Learning Engineer

Recruitment.aiUnited States🇺🇸United StatesPosted Oct 12, 2026

Quick Overview

Seniority

Mid Senior

Work mode

Hybrid

Location

United States

Posted

Yesterday

SQLMachine LearningScikit-learnAgileDeep LearningPandasPyTorchPython

Job Description

In this role, you will:

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.

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