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Ohalo · 1 hour ago

Senior Machine Learning Engineer

South San Francisco

Full-time

Onsite

Senior Level, Lead/Staff

$170K/yr - $220K/yr

5+ years exp

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 and AI-powered hybrid-optimization services.

AgricultureBiotechnologyGenetics

No H1B

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

Qualification

Production ML/AI systemsExpert PythonML framework (JAX/TensorFlow/PyTorch)Cloud data platformsData-engineering pipelinesMicroservicesDocker/KubernetesAdvanced Image TechniquesLeadershipCommunication

Required

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

Preferred

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