Machine Learning Engineer

Orchard Robotics•Seattle, WA

29d•Onsite

About The Position

In order to analyze billions of fruit on farms all year long, our advanced, tractor-mounted camera systems have to know a.) precisely where they are, and b.) everything about the fruit they are seeing. We are looking for a Machine Learning Engineer to build creative, practical, and robust solutions to ML/CV software and infrastructure problems, relating to training edge ML models on massive amounts of real-world farm image data collected by our camera systems. About the role: Full-time, in-person role at our San Francisco or Seattle office. As an early engineer, you'll receive generous equity compensation Comprehensive Health, Vision, and Dental coverage, and we cover 100% of the premium We move fast, and sometimes this means staying late or working weekends Our team is close-knit & highly driven, you’ll work directly with our CEO and entire team We’re deeply motivated by the impact we’re making – every line of code written or new system built means less food that goes to waste, and more people who are fed.

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.

Responsibilities

Build and maintain scalable ETL pipelines for processing large, diverse image datasets collected from our 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.