Machine Learning Engineer

Position Overview:

MyOme is a CLIA-certified clinical whole-genome sequencing company building AI risk models that combine clinical data with genomics to predict disease risk years before onset. We are hiring a Machine Learning Engineer with deep expertise in machine learning and advanced deep learning model development. You will develop and improve deep learning risk models and own the path from trained model to a reliable, monitored, clinically reportable service, applying modern AI (LLM and agentic) methods where they add value. You will work directly with model developers and with clinical, regulatory, product, and engineering teams.

What You'll Do:

• Develop and improve deep learning risk models: architecture design, multimodal EHR + genomics fusion, temporal and sequence modeling of patient histories, deep survival models, representation learning, and rigorous ablation and benchmarking.
• Productionize disease risk models as versioned bundles (model weights, survival model coefficients, calibration, PRS scaling), with reproducible training and a model registry.
• Build and maintain inference services (Python, FastAPI, Docker) and integrate them with MyOme's reporting and data platforms.
• Implement calibration, recalibration to new populations, and score reporting (absolute risk over defined horizons, percentiles, risk tiers).
• Stand up deployment monitoring for calibration, discrimination, input drift, and coverage, with defined retraining triggers.
• Run and automate external validation on partner datasets in secure environments.
• Coordinate model evaluation at clinical trial sites with the hiring manager, including site data intake, harmonization, evaluation runs, and results reporting.
• Apply LLMs and agentic methods where they add measurable value, such as structured extraction from clinical notes, automated evaluation, and guardrailed explanation of results.
• Produce validation reports, model cards, and change-control documentation to support CLIA and regulatory review.

What You'll Need:

• MS or PhD in computer science, statistics, biomedical informatics, bioengineering, or a related field (PhD preferred), or equivalent experience; 5+ years developing ML and deep learning models, including at least 3 years with models in production.
• Strong ML fundamentals: supervised learning, survival analysis (Cox and deep survival models), calibration, class imbalance, leakage and cohort shift, and evaluation (AUROC, C-index, calibration curves, decision curves).
• Strong advanced deep learning model development background: designing, training, and debugging modern architectures in PyTorch (transformers, sequence and temporal models, multimodal fusion, deep survival models), with a track record of models that beat strong baselines.
• Rigorous experimental practice: ablations, hyperparameter search, regularization and overfitting control, reproducibility, and honest comparison against baselines.
• Production ML engineering: packaging, APIs, containers, CI/CD, testing, model versioning, and monitoring.
• Experience building and evaluating LLM-based systems (prompting, tool use and agents, retrieval, structured extraction) with quantitative evaluation.
• Strong Python and SQL; comfortable with large tabular datasets and cloud environments (GCP or AWS).
• Clear communicator who can explain model behavior to clinicians and product partners.

Preferred / Bonus Qualifications:

• Experience with EHR data (OMOP, ICD, LOINC, RxNorm, temporal features), ideally All of Us, UK Biobank, or health-system data.
• Familiarity with polygenic risk scores or statistical genetics.
• Experience in regulated environments (CLIA, CAP, HIPAA, FDA SaMD, design controls).
• MLOps tooling (MLflow or similar, Airflow or Prefect, Kubernetes).
• Publications or shipped products in clinical ML.

Location, Compensation, and Benefits:

• Location: Hybrid, 2 to 3 days onsite at our Menlo Park, CA laboratory campus. 
• Compensation: Annual salary range of $170,000 - $200,000, commensurate with experience. This role is eligible for equity.