AI/ML Engineer

About MyOme

MyOme's mission is to provide clinically actionable genetic information to patients throughout their lives. We combine clinical-grade whole genome sequencing, advanced methods for genome interpretation, and seamless digital tools for doctors and patients to order and access results. Out team is composed of seasoned entrepreneurs, scientists, and operators, and we're backed by top-tier investors.

Job Description

We are seeking an AI/ML Engineer to design LLM-powered agents, RAG pipelines, and predictive models that safely and effectively use clinical and genomic data. In this hands-on role, you will architect agentic systems, deliver end-to-end ML solutions from data to deployment, and implement LLMOps/MLOps foundations that ensure reliability, privacy, and interoperability. The work of this role directly impacts our ability to accelerate product development across multiple disease areas. By building robust LLM-powered agents, RAG pipelines, and predictive models, you will help expand our clinical AI solutions into new therapeutic domains, reduce time-to-market for new features, and enable scalable personalization for providers and patients.

This will be an onsite position at our Menlo Park, CA location.

Responsibilities

AI Agents and LLM Systems

• Design and implement agentic systems

• Build RAG pipelines that ground LLMs in curated, up-to-date medical knowledge and EHR data

• Develop, test, and version prompts and tool-use workflows

• Integrate tool calling and APIs

Predictive Modeling and Feature Engineering

• Integrate and improve existing disease-risk and polygenic risk workflows using features derived from structured EHR, clinical notes, labs, and (as needed) imaging.

• Apply NLP for clinical text, entity/ontology mapping, and embedding strategies.

• Ensure model calibration, explainability, and fairness across cohorts

LLMOps/MLOps and Productionization

• Establish CI/CD, model and prompt registries, feature stores, and reproducible training/inference pipelines.

• Monitor cost/performance and latency; optimize for GPU/CPU utilization and caching strategies

Collaboration and Communication

• Work cross-functionally (engineering, product, clinical, lab, research) to define requirements, timelines, and success metrics.

• Share progress transparently; surface risks early; contribute to best practices in healthcare AI engineering.

Skills and Experience

• PhD in CS, EE, Bioinformatics, or a related field; OR 5+ years of industry ML experience.

• Proficiency in Python and ML frameworks (PyTorch/TensorFlow); strong software engineering fundamentals.

• Hands-on with LLMs development and fine-tuning, prompt design, function/tool calling, RAG, and vector databases (FAISS, Pinecone, Weaviate).

• Experience with AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen) and building multi-step or multi-agent workflows.

• Strong SQL; experience with distributed data processing (e.g., Spark/Databricks)

• Nice to have: Familiarity with healthcare data standards (HL7 FHIR, OMOP; SNOMED CT, ICD-10/11, LOINC, RxNorm) and their use in ML/RAG pipelines.

• Nice to have: Understanding of HIPAA/GDPR, PHI handling, encryption, RBAC, and audit logging in production systems.
• Nice to have: Genomic data experience; clinical NLP; speech-to-tech; LoRA/PEFT fine-tuning; RHLF/RLAIF; federated learning, imaging models.