Principal Machine Learning Engineer (Contract)

Senior / Principal Machine Learning Engineer - Biotech & Sparse Data Systems

📍 Somerville, MA (Hybrid) | ? Contract Position
👩‍💻 ML Engineering | MLOps | Gene Therapy | AWS | Sparse Data
Only accepting applications from U.S. Citizens or Green Card Holders

iO Associates is partnering with a leading biotech innovator that's redefining how machine learning is applied to complex biological data. We're building a shortlist of high-level Machine Learning Engineers for an upcoming contract position focused on solving some of the toughest challenges in gene therapy using cutting-edge ML systems and tools.
This role is ideal for engineers who want to operate across the full stack of ML engineering-from data pipelines and model deployment to reproducibility and auto-scaling in cloud-native environments.

What You'll Work On:

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  Build, scale, and deploy robust ML pipelines for sparse, high-dimensional biological data
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  Develop and maintain infrastructure for model versioning, traceability, and reproducibility (e.g., DVC, MLflow)
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  Architect systems integrating multiple biological data scales (genomics, proteomics, cellular, etc.)
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  Optimize ML workflows for auto-scaling and deployment in AWS (Lambda, Modal, Batch, etc.)
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  Design user-facing tools and APIs (e.g., Flask) to democratize model access across scientific teams
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  Implement and refine best practices in MLOps, CI/CD, and data lineage tracking
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  Collaborate cross-functionally with data scientists, biologists, and computational teams

You'll Bring:

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  5+ years of experience in ML engineering, software/data engineering, or related roles
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  Strong proficiency in Python, SQL, and cloud platforms (AWS preferred)
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  Experience with ML frameworks like PyTorch, TensorFlow, or JAX
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  Expertise in containerization (Docker) and orchestration tools (Kubernetes, Airflow, Dagster, Slurm)
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  Hands-on experience with MLOps tools such as MLflow, Kubeflow, Ray
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  Solid understanding of version control, CI/CD pipelines, and reproducible research workflows

Bonus Experience (Not Required):

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  Familiarity with GraphQL, vector/graph databases, or knowledge graphs
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  Experience with federated learning or ML for gene therapy and drug discovery
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  Exposure to distributed computing frameworks (e.g., Apache Spark)
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  Experience deploying models on edge devices or in resource-constrained settings

Why Consider This Role?

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  Collaborate on novel gene therapy research with direct real-world applications
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  Tackle some of the hardest problems in ML for biological systems
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  Competitive contract rate + benefits (Medical, Dental, Vision)
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  Flexible hybrid work with an on-site component in Somerville, MA
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  Work with a team that values mentorship, innovation, and technical rigor

Important Notes:

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  This is a contract position, initially scoped at 6-12 months, with extension potential
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  Applicants must be U.S. Citizens or Green Card Holders
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  Preference for candidates who can be onsite at least part-time in Somerville, MA

📧 Interested or want to learn more? Send your CV or reach out directly. Whether you're actively looking or just exploring next steps in ML engineering for biotech, we'd love to hear from you.