Senior Machine Learning Research Scientist (AI)

Location: Hybrid in commuting distance of a hirify.global location, with on-site work an average of 2.5 days per week. Available locations include Bothell, Washington; San Diego, California; Cambridge, Massachusetts; Groton, Connecticut; Pearl River, New York; and New York City, New York. Candidates must have permanent authorization to work in the United States.

Annual base salary: $106,000–$176,600, plus eligibility for a 15% Global Performance Plan bonus and share-based long-term incentives.

Company

hirify.global develops medicines and vaccines and is building AI capabilities for biomedical research, drug discovery, and development.

What you will do

• Develop, adapt, and improve foundation models, multimodal and graph-based methods, generative models, and agentic reasoning systems for biomedical research.
• Design controlled, reproducible experiments and compare competing approaches using hypothesis-driven investigations.
• Create datasets, benchmarks, and validation strategies covering robustness, calibration, generalization, transfer, interpretability, reasoning, and failure modes.
• Translate model results into scientific evidence and recommendations with AI engineers, computational biologists, clinical experts, and domain specialists.
• Build reusable model components, workflows, benchmarks, and evaluation frameworks that can be applied across diseases, modalities, and teams.
• Contribute to scientific reviews and research planning while mentoring colleagues and improving AI fluency across R&D.

Requirements

• PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field preferred; alternatively, a Master's degree with 2+ years of relevant industry or research experience.
• Experience developing, extending, or evaluating machine learning methodologies for difficult, ambiguous, high-value problems.
• Experience designing rigorous experiments, conducting hypothesis-driven investigations, and building and comparing models through reproducible scientific approaches.
• Ability to identify limitations, bias, uncertainty, and failure modes and translate research insights into meaningful outcomes and recommendations.
• Experience owning a research area, benchmark, model component, or scientific capability from concept through delivery, with measurable scientific, product, or business impact.
• Strong Python and PyTorch, JAX, or an equivalent framework.
• Knowledge of model development, optimization, debugging, evaluation methodologies, experimental design, statistical analysis, and reproducible research practices.
• Familiarity with scalable ML workflows, including distributed training, experiment tracking, and shared evaluation pipelines.
• Depth in one or more of multimodal learning, foundation models, GNNs, generative AI, agentic systems, biological sequence or structure modeling, knowledge graphs, clinical AI, omics and biomedical data, or scientific ML.
• Intellectual curiosity, analytical reasoning, comfort with ambiguity, willingness to challenge assumptions, and the ability to influence through credibility and execution.
• Permanent U.S. work authorization is required; U.S. work visa sponsorship is not available now or in the future.

Nice to have

• Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research.
• Experience with biological, molecular, imaging, clinical, or real-world health data.
• Experience building foundation models or multimodal learning systems for scientific applications.
• Experience evaluating and implementing responsible AI methodologies.
• Experience working across multidisciplinary scientific organizations.
• Publications, patents, open-source contributions, deployed research, or other evidence of scientific impact.

Culture & Benefits

• Scientific autonomy and ownership of high-impact research problems supported by proprietary biomedical data.
• Collaboration with AI researchers, computational biologists, engineers, clinical experts, and scientific partners.
• Hybrid work with an average of 2.5 on-site days per week.
• Benefits include 401(k) matching and retirement contributions, paid vacation, holidays, personal days, caregiver/parental and medical leave, and medical, prescription, dental, and vision coverage.
• Relocation assistance may be available based on business needs and eligibility.