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Latent is building an enterprise pharmacy intelligence platform. We’re hiring a Machine Learning Engineer, Research, to own design and development of novel modeling approaches that advance clinical intelligence and impact patient care.
Senior and staff engineers will lead research end-to-end, collaborate with clinicians and engineers, and translate research into deployable systems that operate on longitudinal patient data.
Latent is the enterprise pharmacy intelligence platform. Our clinical AI streamlines prior authorizations, appeals, and 340B compliance, so patients start therapy faster and care teams spend less time on paperwork.
We raised an $80M Series A led by Spark Capital and Transformation Capital, with General Catalyst, McKesson Ventures, Conviction, and Y Combinator. 60+ health systems run on Latent, including Yale New Haven, Mount Sinai, UCSF, and Ochsner. We move fast, operate with high ownership, and build products that directly improve patient care.
As a Machine Learning Engineer, Research, you will own the design and development of novel modeling approaches that advance state-of-the-art clinical intelligence.
You will drive research from ambiguous problem definition through to validated results and downstream impact, shaping the technical direction of how models learn from longitudinal patient data.
We are primarily hiring for senior and staff-level engineers who are comfortable owning critical research problems end-to-end.
This role involves working on problems that directly impact real patient outcomes.
Own research initiatives end-to-end, including problem formulation, experimental design, modeling, and evaluation
Develop novel architectures, training methods, and objectives leveraging longitudinal patient data
Work on verifiable reinforcement learning, mid-training, and post-training of foundation models
Design rigorous evaluation methodologies to assess model reasoning, correctness, and clinical relevance
Make and own tradeoffs between model capability, interpretability, and verifiability in high-stakes settings
Collaborate with clinicians and engineers to define meaningful problem formulations grounded in real-world workflows
Partner with ML engineers to ensure research translates into deployable systems
Strong foundation in machine learning, deep learning, or a related technical field
Track record of driving ML research or novel modeling work from idea to validated results
Experience working on ambiguous research problems with limited prior art
Hands-on experience with PyTorch or similar frameworks
Ability to operate independently in high-ambiguity environments with minimal guidance
Strong technical judgment — you can identify meaningful problems, design appropriate approaches, and evaluate results rigorously
Comfort working in a fast-moving, early-stage environment
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)
Publications at top-tier ML venues (e.g., NeurIPS, ICML, ICLR)
Experience with LLMs, NLP, or sequence modeling
Experience with reinforcement learning or alignment methods
Experience working with longitudinal or structured data at scale
Experience working with clinical, biomedical, or scientific domains
Backed by top-tier investors including General Catalyst, Conviction, and Y Combinator
Work alongside a high-caliber team building products our healthcare partners love
Work on mission-critical problems at the intersection of AI and healthcare
Real ownership and visibility
High-impact role on a small, fast-growing team
Competitive compensation, including meaningful equity
Medical, dental, and vision insurance for employee and dependents
Flexible PTO policy
Paid parental leave
Fertility and family-building stipend through Carrot