Machine Learning Engineering LeadOhalo • San Francisco, CA, US
Machine Learning Engineering Lead
Ohalo • San Francisco, CA, US
[job_card.30_days_ago]
[job_preview.job_type]
- [job_card.full_time]
[job_card.job_description]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. 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.
Job Description
Job Description
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
About Ohalo :
Ohalo™ aims to accelerate evolution to unlock nature's potential. Founded in 2019, Ohalo develops novel breeding systems and improved plant varieties that help farmers grow more food with fewer natural resources, increasing the yield, resiliency, and genetic diversity of crops to sustainably feed our population. Ohalo's breakthrough technology, Boosted Breeding™, will usher in a new era of improved productivity to radically transform global agriculture. For more information, visit www.ohalo.com.
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 :
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