Machine Learning Software Engineer III

ML Software Engineer IIILocation: Florence, CO or Denver/Boulder. Remote considered for exceptional candidates.Job Type: Full-TimeCompensation: $145k-175k + meaningful equity participationAbout 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 OverviewWe’re looking for an ML Software Engineer III to take a leading role in building and scaling the computer vision and perception systems behind our ANT platform.This is a senior applied ML engineering role focused on real-world perception performance. You’ll own major parts of the ML lifecycle end-to-end - from dataset development and model iteration through edge deployment, sensor integration, in-field validation, and long-term system reliability. You’ll work closely with robotics, autonomy, and hardware teams to ensure our perception systems perform reliably 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 perception engineering, driving sound system 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 crops, weeds, and the surrounding environment safely and reliably in production.Key ResponsibilitiesML Development & DeploymentOwn the design and optimization of computer vision models for real-time performance on edge devicesLead model optimization for latency, memory, and hardware accelerationDefine evaluation frameworks and ensure performance translates to real-world field conditionsDebug and resolve production ML issues in-field, driving rapid iterationShape ML system architecture, experimentation, and reproducibilityData & Model LifecycleOwn the end-to-end data lifecycle - collection, labeling, curation, and versioningDefine data strategies to improve model performance, including edge case discovery and feedback from field dataEnsure high-quality datasets with strong coverage across real-world conditionsSoftware EngineeringWrite and maintain production-quality software with appropriate testing, logging, and observability to support reliable ML-driven systemsImprove system performance, scalability, and reliability across the ML stackLead debugging and root cause analysis across ML, data, and system-level issuesSet and uphold engineering best practices, including testing, code quality, and documentationSystem Integration & RoboticsIntegrate 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 actuationOptimize end-to-end system performance across sensing, inference, and decision-making loopsDebug and resolve system-level issues across ML, sensors, and robotics pipelines in both lab and field environmentsSuccess Metrics (First 12-18 Months)Successfully deploy and iterate on ML models used in production ANT field operationsImprove perception accuracy and robustness across multiple crops and environmentsMaintain a reliable ML pipeline that evolves in line with production dataReduce field issues caused by ML failures through better testing and iterationImprove end-to-end autonomy performance by delivering dependable ML componentsRequired Qualifications8+ years of professional software engineering experience with hands-on ML systemsStrong proficiency in Python and deep experience with modern ML frameworksProven track record of deploying ML models into reliable, production-grade systemsDeep understanding of CV fundamentals, model evaluation, and real-world performance tradeoffsAbility to design and own software components that support ML-driven systems at scaleComfortable operating in ambiguity, working with real-world data, and driving iterative, field-driven developmentBonus PointsExperience with object detection or segmentation models (e.g. YOLO or similar)Familiarity with edge deployment and model optimization for constrained hardwareExposure to robotics, autonomy, or real-time systemsExperience working with ROS2 or integrating ML into larger distributed systemsBackground in outdoor, agricultural, or other field-deployed ML systemsOur CultureAt 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 UsMission-Driven Work: Build robots that transform farming and rural economiesReal-World Impact: See your engineering work deployed in active farm operationsHands-On Innovation: Work directly on full-stack robotics systemsFast Learning Curve: Collaborate across hardware, software, and autonomy to expand your technical range, skills and experienceEquity & Growth: Share in the company’s success at scaleHow to ApplySend your resume, and a few lines about why this role excites you to