Senior Machine Learning Engineer

Ohalo • South San Francisco, CA

24d • $170,000 - $220,000

About The Position

Ohalo is looking for a hands-on Senior Machine Learning Engineer / Lead to convert cutting-edge quantitative-genetics and computer vision research into production systems that accelerate crop improvement. You will steer a 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.

Requirements

• Education – M.S. in Computer Science or related field (or equivalent industry record).
• Experience – 5+ years building production ML/AI systems, technical lead experience preferred
• 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.

Nice To Haves

• Advanced Image Techniques – Proficiency in leveraging Foundation Models (e.g., SAM, CLIP) and Self-Supervised Learning to automate high-throughput feature extraction, converting unstructured imagery into high-dimensional embeddings for advanced statistical analysis.

Responsibilities

• Design, build, and maintain scalable ML pipelines on GCP (or the best-fit cloud) using Python, BigQuery/Spark, Kubernetes, and CI/CD best practices.
• 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.
• Establish MLOps Excellence: Build robust infrastructure for model versioning, data lineage, and automated retraining; implement "champion-challenger" deployment patterns to safely promote research models to production while ensuring full auditability of breeding decisions.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level