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.