ML Software Engineer II

IMPORTANT: please read the Required Qualifications carefully - we will not review your application unless your resume/Linked In profile appears to satisfy these role requirements

Location: Florence, Colorado (in-office with flexibility for hybrid work in Denver/Boulder/Colorado Springs)

Job Type: Full-Time

Compensation: $120k-150k + meaningful equity participation

About Barn Owl Precision Ag (BOPA)

At BOPA, we’re building the future of autonomy for small and mid-sized farms. Our compact, intelligent robots (ANTs) perform precision agricultural tasks like weeding, planting, and nutrient management - helping farmers cut labor costs, reduce chemical use, and increase sustainability.

We are a Seed-stage startup with a nimble, farmer-focused team. Our goal is to design robust, scalable robotics systems that can be deployed across the globe.

We’re looking for a hands-on Software Engineer with a focus on Machine Learning to help build, deploy, and improve perception and ML-driven systems powering our autonomous robots.

This is an applied ML role - not pure research. You’ll work closely with robotics, autonomy, and field operations to take models from data → training → deployment → real-world validation. Your work will directly affect how ANTs see crops, detect weeds, and operate reliably in messy, unstructured outdoor environments.

Ideal for an engineer who enjoys shipping ML into production, debugging model behavior in the real world, and iterating quickly based on field feedback.

ML Development & Deployment

• Develop, train, and evaluate ML models for perception and classification tasks
• Integrate ML models into the ANT autonomy stack for real-time edge inference
• Integrate ML-driven decision logic that directly trigger in-field robot actions (e.g. commanding weeding blades based on perception output)
• Optimize models for performance, reliability, and deployment constraints
• Maintain and improve the end-to-end ML pipeline, including data ingestion, training workflows, evaluation, and deployment to production systems

Data & Model Lifecycle

• Work with field-collected data to improve training datasets
• Support labeling workflows, dataset versioning, and experiment tracking
• Analyze model performance in real-world conditions and drive iterative improvements

Software Engineering

• Write and maintain production-quality software with appropriate testing, logging, and observability to support reliable ML-driven systems
• Design and evolve ML-related software components with an eye toward scalability, maintainability, and clear interfaces
• Collaborate with robotics and systems engineers to integrate ML outputs into control and decision-making systems

Field Testing & Validation

• Participate in field trials to validate model behavior and diagnose failures
• Debug edge cases caused by lighting, weather, crop variability, and sensor noise
• Translate field learnings into concrete model and pipeline improvements

• Successfully deploy and iterate on ML models used in production ANT field operations
• Improve perception accuracy and robustness across multiple crops and environments
• Maintain a reliable ML pipeline that evolves in line with production data
• Reduce field issues caused by ML failures through better testing and iteration
• Improve end-to-end autonomy performance by delivering dependable ML components

• 5+ years of professional software engineering experience with hands-on ML systems
• Strong proficiency in Python and experience with modern ML frameworks
• Experience deploying ML models into live production systems, not just research
• Solid understanding of CV fundamentals and model evaluation techniques
• Ability to design and maintain software components that support ML-driven systems
• Comfort working with real-world data, ambiguity, and iterative, field-driven development

• Experience with object detection or segmentation models (e.g. YOLO or similar)
• Familiarity with edge deployment and model optimization for constrained hardware
• Exposure to robotics, autonomy, or real-time systems
• Experience working with ROS2 or integrating ML into larger distributed systems
• Background in outdoor, agricultural, or other field-deployed ML systems

At BOPA, we value practical impact, humility, and speed of iteration. We test everything in the field, learn fast, and build with farmers. We believe diverse perspectives lead to better designs, and we’re committed to fostering inclusion and collaboration.

• Mission-Driven Work:
  Build robots that transform farming and rural economies
• Real-World Impact:
  See your engineering work deployed in active farm operations
• Hands-On Innovation:
  Work directly on full-stack robotics systems
• Fast Learning Curve:
  Collaborate across hardware, software, and autonomy to expand your technical range, skills and experience
• Equity & Growth:
  Share in the company’s success at scale