Machine Learning Engineering Lead

Position Title: Machine Learning Engineering Lead
Location: San Francisco, CA
Time Type: Full Time

The Opportunity

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 Bayesian genomic-prediction pipelines, breeding-system simulations, and AI-powered hybrid-optimization services. Your work will directly shape how breeders make thousands of crossing decisions and drive the next leap in agricultural productivity.

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.

Candidate Profile

• Education – M.S. in Computer Science, Statistics, Quantitative Genetics, or related field (or equivalent industry record).
• Experience – 3–5 years building production ML/AI systems, with at least 1–2 years in a technical-lead or mentoring role.
• Genomic & statistical depth – Practical experience with genomic-prediction, GWAS, or related quantitative-genetics models; proficiency in Bayesian/MCMC methods.
• Engineering excellence – 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.).
• Data-engineering acumen – Comfortable designing batch, streaming, and event-driven pipelines; Pub/Sub, Kafka, or equivalent.
• Leadership & communication – Able to set direction, give candid feedback, and bridge domain-scientist ↔ engineer conversations with clarity.
• Bonus points for: Heterotic-group analysis, trait-mapping for fertility, Nextflow pipelines, or high-throughput phenotype imaging.

The anticipated pay range for this role is $140,000 - $180,000 per year for our San Francisco, CA location, though salary will be based on a variety of factors including, but not limited to, experience, skills, education, and location.

Notes:

• If you previously applied for a job at Ohalo, we encourage you to restate your interest in the position by submitting your application.
• No visa sponsorship is available for this position at this time.
• No recruiters please.