Machine Learning Software Engineer IIIAlpine Recruiting Company • Florence, South Carolina, United States

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Machine Learning Software Engineer III

Alpine Recruiting Company

Florence, South Carolina, United States

Florence, South Carolina, United States

Jetzt Bewerben

Über

ML Software Engineer III 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 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 & 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 How to Apply Send your resume, and a few lines about why this role excites you to recruiting@barnowlag.com

Florence, South Carolina, United States

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English

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