Machine Learning Scientist I / II, Protein Design

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Company Overview

A life sciences technology organization is building a platform where AI and automation work together to address complex challenges in medicine. Within its Life Sciences AI organization, the AI for Protein Engineering team develops models and systems designed to take biologics from initial design specifications through wet-lab validation.

The organization is seeking a Machine Learning Scientist I / II, Protein Design to design molecules for active biologics programs and transform insights from individual programs into reliable, extensible capabilities that can generalize across multiple programs.

The successful candidate will combine strong machine learning and software engineering skills with biological intuition and protein design expertise. This role will involve close collaboration with domain scientists, platform teams, and AI researchers to connect specialized protein design models with a broader autonomous science platform.

Key Responsibilities

• Design molecules for active biologics programs in partnership with domain scientists.
• Translate target characteristics, mechanisms, and experimental constraints into actionable molecular design hypotheses.
• Develop reasoning capabilities for drug discovery and orchestrate complex design workflows.
• Design and maintain benchmarks and evaluation infrastructure to measure the effectiveness and generalizability of design workflows across biologics programs.
• Establish evaluation methodologies that determine whether automated design systems are producing useful and reliable decisions.
• Own operational processes related to reproducibility, throughput, and inference costs for computational design workflows.
• Work closely with domain scientists to understand how molecular designs are prioritized.
• Translate expert scientific judgment into machine learning objectives and evaluation criteria.
• Develop reliable and extensible systems that improve molecular design capabilities across multiple programs.
• Collaborate with AI researchers and platform teams to integrate specialist protein design models into broader autonomous science systems.

Required Qualifications

• Master’s or PhD degree in Computer Science, Machine Learning, Computational Biology, Biophysics, Bioengineering, or a related quantitative field.
• Strong software engineering and system design fundamentals.
• Strong understanding of machine learning evaluation and dataset design.
• Ability to identify benchmark leakage and understand why strong validation results may not translate into successful downstream outcomes.
• Experience developing rigorous approaches to measuring whether automated systems make effective decisions.
• Strong cross-functional communication and collaboration skills.
• Domain expertise in protein sequence, structure, and function.
• Strong biological intuition combined with the ability to apply machine learning to real-world protein design challenges.

Preferred Qualifications

• Experience building reasoning models, AI agents, planning systems, or multi-step machine learning orchestration.
• Experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins within design-test-learn cycles.
• Experience developing evaluation harnesses, model registries, or benchmark suites.
• Experience training or serving machine learning models at scale.
• Experience with distributed training, GPU optimization, and high-throughput inference.
• Publications, open-source contributions, or applied research outputs in AI for Science.
• Experience translating research insights into reusable systems and capabilities across multiple scientific programs.

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