Machine Learning Engineer, Research (Healthcare AI)

Location: San Francisco, on-site; work together in person and spend most of the week in the office

Base salary: $225,000–$300,000+

Company

hirify.global Health builds AI systems for personalized healthcare by combining population-level clinical knowledge with longitudinal individual patient history.

What you will do

• Own machine learning research initiatives from problem formulation and experimental design through modeling, evaluation, and validated results.
• Develop novel architectures, training methods, and objectives using longitudinal patient data.
• Work on verifiable reinforcement learning, mid-training, and post-training of foundation models.
• Design rigorous evaluations of model reasoning, correctness, clinical relevance, interpretability, and verifiability.
• Collaborate with clinicians and engineers to define workflow-grounded problems and translate research into deployable systems.
• Own critical research systems and technical tradeoffs that affect real patient outcomes.

Requirements

• Strong foundation in machine learning, deep learning, or a related technical field.
• Experience driving ML research or novel modeling work from idea to validated results.
• Experience solving ambiguous research problems with limited prior art.
• Hands-on experience with PyTorch or similar frameworks.
• Ability to work independently in high-ambiguity environments with strong technical judgment.
• Experience with systems where decisions have real-world consequences, such as healthcare, finance, or infrastructure.

Nice to have

• Publications at top-tier ML venues such as NeurIPS, ICML, or ICLR.
• Experience with LLMs, NLP, sequence modeling, reinforcement learning, or alignment methods.
• Experience with longitudinal or structured data at scale.
• Experience in clinical, biomedical, or scientific domains.

Culture & Benefits

• Work on high-stakes problems with direct impact on patient care.
• Build systems that define how AI is trusted in clinical decision-making.
• Take significant ownership in a small team of researchers and engineers.
• Competitive compensation and meaningful equity in an early-stage Series A company.
• In-person collaboration in the San Francisco office.