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.

• 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.