Senior Machine Learning Engineer

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Company Overview

A rapidly growing technology company is focused on securing America’s food supply through artificial intelligence and agricultural technology. The organization is developing an AI-powered platform designed to automate and improve farming operations by combining advanced computer vision, machine learning, robotics, and farm-management software.

The Role

The organization is seeking a Senior Machine Learning Engineer to develop creative, practical, and reliable solutions to machine learning, computer vision, and infrastructure challenges. The role will focus on training and deploying edge machine learning models using massive amounts of real-world agricultural image data collected from tractor-mounted camera systems.

The successful candidate will work closely with engineering, robotics, agricultural, and leadership teams to develop systems capable of analyzing billions of pieces of agricultural data throughout the year. The position requires strong technical expertise, adaptability, and the ability to work directly with real-world farming environments.

Key Responsibilities

• Build and maintain scalable ETL pipelines for processing large and diverse image datasets collected from tractor-mounted camera systems.
• Stay current with developments in computer vision models and architectures and apply relevant advancements to production systems.
• Develop, deploy, and monitor infrastructure for machine learning model training, evaluation, and inference across cloud and edge environments.
• Design and implement intelligent active-sampling infrastructure to optimize data collection and improve model performance.
• Collaborate with multidisciplinary teams to integrate machine learning solutions into production robotics systems.
• Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable machine learning features.
• Contribute across different areas of the software stack as needed.
• Develop robust machine learning infrastructure capable of supporting large-scale agricultural datasets.
• Help improve the reliability, scalability, and performance of machine learning systems deployed in real-world environments.

Required Qualifications

• 5+ years of experience building production-grade data pipelines and machine learning infrastructure.
• Proficiency in Python.
• Experience with machine learning frameworks such as TensorFlow or PyTorch.
• Strong experience with data engineering tools including Pandas, SQL, Apache Airflow, and Spark.
• Familiarity with cloud platforms such as AWS, GCP, or Azure.
• Experience with containerization technologies such as Docker and Kubernetes.
• Experience working with massive amounts of real-world training data.
• Familiarity with MLOps practices and data engineering processes supporting consistent machine learning model deployment.
• Ability to work independently, learn quickly, and operate effectively in a dynamic environment.
• Willingness to take on multiple responsibilities as the organization grows.

Work Environment and Culture

• Full-time, five-day-per-week, in-person position based in San Francisco, California.
• Employees work closely with a highly driven and collaborative team.
• The role provides opportunities to work directly with senior leadership and contribute to high-impact technical initiatives.
• The organization emphasizes resilience, rapid execution, continuous improvement, customer focus, and ownership.
• Team members are expected to remain closely connected to agricultural operations and spend time working alongside farmers in the field.
• The organization promotes a low-ego culture in which employees take ownership of outcomes and contribute wherever needed.
• The role may occasionally require extended hours or weekend work based on business and project requirements.

Benefits

• Comprehensive health, vision, and dental coverage.
• 100% of applicable premiums covered by the organization.
• Equity opportunities.
• Opportunity to contribute to technology focused on reducing food waste and improving agricultural productivity.

Disclaimer

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