Machine Learning Engineer, Research
Latent
Machine Learning Engineer, Research
Latent
Automates prior authorization via clinical AI
Latent is the enterprise pharmacy intelligence platform, powered by a clinical agentic engine, that enables health systems to grow their pharmacy operations and serve more patients. One engine works across the pharmacy journey to identify patients who qualify for care, improve medication access, capture eligible prescriptions and support patients throughout treatment. Latent’s platform enables more than 60 leading health systems that collectively serve an estimated 70 million people. Founded by Sriram “Sri” Somasundaram and Rishabh Jain, Latent has raised $80 million from Transformation Capital, Spark Capital, Conviction, General Catalyst, Y Combinator, and others.
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
Latent Health creates a Clinical Reasoning Engine that works with electronic health records, payer guidelines, and pharmacy operations to automate prior authorization and other medication-access tasks. It analyzes unstructured clinical data like doctors' notes and lab results to find the evidence needed for insurer approvals and to answer questions about a patient’s medical history. The system can compile and submit authorization requests after clinician review and even call insurers to check on status, reducing manual work and speeding treatment. By tightly integrating EHRs, payer rules, and pharmacy workflows, Latent Health aims to reduce denials, increase how many patients can be treated, and shorten the time from decision to therapy.
Explore more opportunities at Latent