Machine Learning Research Scientist


Cambridge
Permanent
Negotiable
Biometrics
PR/543146_1745602996
Machine Learning Research Scientist

Job Description: Machine Learning Scientist - Computational Biologics Discovery

We are working with a cutting-edge biomedicines company at the intersection of machine learning, protein engineering, and immunology. This innovative team is building a frontier AI platform to accelerate the computational discovery and optimization of next-generation biologic drugs. Backed by top investors and driven by a superstar group of ML scientists, structural biologists, and immunologists, our client is on a mission to translate audacious ideas into breakthrough therapies for immune-mediated diseases.

Position: Machine Learning Scientist - Computational Biologics Discovery
Location: On-site
Type: Full-Time

What You'll Do

  • Model Innovation: Design and train novel ML models that optimize natural proteins for therapeutic properties and engineer entirely new classes of synthetic proteins with targeted functions.

  • De Novo Antibody Design: Lead computational antibody design efforts to generate high-affinity candidates against challenging antigens.

  • Data Integration: Fuse diverse data sources-protein sequence, structure, functional assays, and molecular characterization-to drive multi-objective optimization of protein drug candidates.

  • Cross-Functional Collaboration: Partner with wet-lab scientists, immunologists, and clinical experts to iteratively refine in-silico designs and accelerate their translation into experimental validation.

  • Infrastructure & Deployment: Establish and maintain robust data infrastructure and production-quality codebases for model training, deployment, and interactive visualization tools.

  • Communication & Reporting: Present scientific progress in regular team meetings and prepare clear, compelling reports and slide decks for internal and external stakeholders.

Who You Are

  • Scientific & Technical Expertise: PhD in Computational Biology, Computer Science, Statistics, or a related field-or equivalent computational experience. Deep knowledge of protein biochemistry, molecular biology, and ML theory.

  • Proven Track Record: 3+ years of industry experience applying deep learning (e.g., variational autoencoders, graph neural networks) to protein engineering, drug discovery, or related domains.

  • Hands-On Protein Design: Experience with sequence- and structure-based generative models (e.g., LLMs for sequences, diffusion/inverse-folding models) and familiarity with real-world lab workflows.

  • Collaboration & Communication: Demonstrated ability to bridge dry-lab and wet-lab teams, translating complex ML outputs into actionable experimental plans and conveying results to diverse audiences.

  • Entrepreneurial Mindset: Self-driven, adaptable, and comfortable working with high-level objectives in a fast-paced startup environment.

Nice to Have

  • Experience with cloud computing (AWS, GCP) and containerization (Docker).

  • Familiarity with interactive app frameworks (Dash, R Shiny).

  • Prior work in immunology or genomics, with publications in relevant journals.

What They Offer

  • High Impact: Directly contribute to a platform that speeds the creation of novel biologic drugs and improves patient outcomes.

  • Collaborative Culture: Join a supportive, interdisciplinary team that values creativity, scientific rigor, and patient-centric innovation.

  • Competitive Package: Attractive salary, equity, and comprehensive benefits.

  • Career Growth: Opportunities for leadership, mentorship, and professional development in a rapidly growing startup.

  • Mission-Driven Environment: Work every day toward delivering breakthrough therapies for immune-system diseases.

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