Location: Florence, CO or Denver/Boulder. Remote considered for exceptional candidates. Job Type: Full-Time Compensation: $145k-175k + 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.
Role Overview
We’re looking for a Software Engineer III (Machine Learning) to take a leading role in building and scaling the perception and ML systems behind our ANT platform.
This is a senior applied ML engineering role focused on production impact. You’ll own major parts of the ML lifecycle end-to-end - from dataset and model iteration to edge deployment, in-field validation, and long-term system reliability. You’ll work closely with robotics, autonomy, and field teams to ensure our ML systems perform under the messy, variable conditions of real farms, not just in controlled environments.
Beyond strong individual contribution, this role requires technical leadership: raising the quality bar for ML engineering, driving sound design decisions, and helping evolve the tooling, architecture, and practices needed to scale ML across our platform. Your work will directly shape how ANTs perceive the world and act on it safely, accurately, and consistently in production.
Key Responsibilities
ML Development & Deployment
Own the design and optimization of computer vision models for real-time performance on edge devices
Lead model optimization for latency, memory, and hardware acceleration
Define evaluation frameworks and ensure performance translates to real-world field conditions
Debug and resolve production ML issues in-field, driving rapid iteration
Shape ML system architecture, experimentation, and reproducibility
Data & Model Lifecycle
Own the end-to-end data lifecycle - collection, labeling, curation, and versioning
Define data strategies to improve model performance, including edge case discovery and feedback from field data
Ensure high-quality datasets with strong coverage across real-world conditions
Software Engineering
Write and maintain production-quality software with appropriate testing, logging, and observability to support reliable ML-driven systems
Improve system performance, scalability, and reliability across the ML stack
Lead debugging and root cause analysis across ML, data, and system-level issues
Set and uphold engineering best practices, including testing, code quality, and documentation
System Integration & Robotics
Integrate ML models into the robotics stack (ROS2), ensuring reliable real-time performance on edge hardware (Jetson/AGX)
Work closely with the hardware team to ensure seamless interaction between perception and actuation
Optimize end-to-end system performance across sensing, inference, and decision-making loops
Debug and resolve system-level issues across ML, sensors, and robotics pipelines in both lab and field environments
Success Metrics (First 12-18 Months)
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
Required Qualifications
8+ years of professional software engineering experience with hands‑on ML systems
Strong proficiency in Python and deep experience with modern ML frameworks
Proven track record of deploying ML models into reliable, production‑grade systems
Deep understanding of CV fundamentals, model evaluation, and real‑world performance tradeoffs
Ability to design and own software components that support ML‑driven systems at scale
Comfortable operating in ambiguity, working with real‑world data, and driving iterative, field‑driven development
Bonus Points
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
Our Culture
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.
Why Join Us
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
**Disclaimer: The duties and responsibilities described above are not a comprehensive list and additional tasks may be assigned to the employee, time to time; the scope of the job may change as necessitated by business demands.
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