Machine Learning Engineer

Responsibilities

• Build and maintain scalable ETL pipelines for processing large, diverse image datasets collected from tractor-mounted camera systems in farms.
• Develop and deploy infrastructure for model training, evaluation, and inference, both in the cloud and on edge devices.
• Design and implement intelligent active sampling infrastructure to optimize data collection and improve model performance.
• Stay up-to-date with current literature in computer vision models and architectures, and apply relevant advancements to our systems.
• Collaborate with a multidisciplinary team to integrate ML solutions into production robotics systems.
• Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable ML features.
• Be a generalist, supporting different parts of our software stack as needed.

Requirements

• 2+ years of real-world, industry experience building production-grade data pipelines and ML infrastructure.
• Proficiency in Python and experience with ML frameworks (e.g., PyTorch).
• Strong experience with data engineering tools (e.g., Pandas, SQL, MLFlow, WandB).
• Familiarity with cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes).
• Experience working with massive amounts of real-world training data.
• Familiarity with MLOps software and data engineering to ensure consistent deployment of ML models.
• Ability to work independently, learn quickly, and operate in a dynamic environment.
• Enthusiasm for taking on multiple roles and responsibilities as our company grows.

Nice-to-Haves

• Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson.
• Experience prototyping, evaluating, or deploying new ML/CV models on the edge.

Compensation & Benefits

• Full-time, in-person role at our San Francisco or Seattle office.
• Generous equity compensation.
• Comprehensive Health, Vision, and Dental coverage (100% premium covered).