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Ohalo · 1 day ago

Machine Learning Engineering Lead

South San Francisco, CA

Full-time

Onsite

Senior Level, Lead/Staff

$140K/yr - $180K/yr

3+ years exp

Ohalo is looking for a hands-on Machine Learning Engineering Lead to convert cutting-edge quantitative-genetics research into production systems that accelerate crop improvement. You will steer a small squad of ML/Data/Software Engineers, partnering with quantitative geneticists and statisticians to deliver advanced genomic-prediction pipelines and AI-powered optimization services.

AgricultureBiotechnologyGenetics

No H1B

Responsibilities

Lead technical strategy & architecture for statistical-genomic services—from MCMC breeding simulations to real-time breeding-value prediction APIs

Design, build, and maintain scalable ML pipelines on GCP (or the best-fit cloud) using Python, BigQuery/Spark, Kubernetes, and CI/CD best practices

Advance statistical rigor by championing Bayesian & mixed-model methods (Stan, PyMC, BGLR, TensorFlow Probability) and ensuring reproducible research-to-production transitions

Integrate genomic technologies : GWAS workflows, marker-assisted selection analytics, heterotic-group analysis, and large-scale phenotype/genotype feature stores

Mentor & grow a small team —provide technical guidance, establish code-review norms, and cultivate a culture of rapid, well-engineered experimentation

Own model-ops lifecycle : automated testing, containerized deployment, continuous monitoring, and A/B evaluation against breeding KPIs

Collaborate cross-functionally with plant scientists, data engineers, and the automation group to ingest high-throughput phenotyping data and close feedback loops

Report progress & roadmap trade-offs directly to the executive team, translating scientific ambition into clear engineering milestones and OKRs

Qualification

Machine LearningPythonBayesian methodsCloud platformsGenomic predictionData engineeringDockerKubernetesLeadershipCommunication

Required

M.S. in Computer Science, Statistics, Quantitative Genetics, or related field (or equivalent industry record)

3–5 years building production ML/AI systems, with at least 1–2 years in a technical-lead or mentoring role

Practical experience with genomic-prediction, GWAS, or related quantitative-genetics models; proficiency in Bayesian/MCMC methods

Expert Python plus one ML framework (JAX/NumPyro, TensorFlow, or PyTorch); strong grasp of microservices, Docker/Kubernetes, and cloud data platforms (BigQuery, Vertex AI, etc.)

Comfortable designing batch, streaming, and event-driven pipelines; Pub/Sub, Kafka, or equivalent

Able to set direction, give candid feedback, and bridge domain-scientist ↔ engineer conversations with clarity