> Markdown version of [/jobs/ext/2689762-ml-perception-engineer](https://www.wearedevelopers.com/jobs/ext/2689762-ml-perception-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Perception Engineer - **Company:** Mytra Corp - **Location:** Brisbane, CA, United States - **Experience:** Expert - **Salary:** $190,000.0 - $205,000.0 - **Contract:** Permanent contract - **Skills:** Computer Vision, C++ (Programming Language), Cloud Storage, Code Review, Information Engineering, Extract Transform Load (ETL), Software Debugging, Python (Programming Language), Object Detection, Robotic Automation Software, Systems Integration, Datadog, Google Cloud, Pytorch, Large Language Models, Deep Learning, TensorRT, Data Pipelines - **Published:** September 3, 2026 - **Apply:** https://www.dice.com/job-detail/dac639b1-4624-453c-86aa-b68699697bc5 ## About the Role * 5+ years of directly relevant experience building and shipping computer vision or ML perception systems (BS required; MS or PhD preferred). * Strong applied computer vision and deep learning skills (detection, segmentation, tracking, depth estimation) together with solid classical and geometric CV, including camera calibration and projection geometry. * A track record of shipping perception to production on real systems, with the data-centric debugging instincts to know why models break and how to fix them. * Hands-on experience with cameras and depth/ToF sensors, including data collection and calibration. * Experience building or operating vision data pipelines and labeling workflows: dataset creation, curation, and assisted or auto-labeling. * Strong Python and PyTorch skills, and comfort with C/C++ for on-device or performance-critical code. * Simulation and sim-to-real experience (Isaac Sim, Webots, Gym, MuJoCo, or similar). * The ability to work across disciplinary boundaries, collaborating with software, controls, and safety engineers to debug real robotic systems. * Comfort with ambiguity and an early-stage mindset: you're excited to shape an immature, high-impact area, and you're willing to realize mistakes and pivot. Nice to have: * Robotics and systems integration experience (ROS or similar, CAN, pub/sub frameworks) and deploying perception across distributed robot fleets. * Edge and embedded model optimization (quantization, pruning, distillation, TensorRT or similar) for resource-constrained on-robot compute. * Foundation models, world models, or vision-language approaches applied to perception or simulation. * Familiarity with vision data and observability tooling (Foxglove, Google Cloud Platform, Encord, or equivalents). ## Description As a Senior ML Perception Engineer at Mytra, you'll be a key member of our Computer Vision team, building the perception stack that gives our distributed robot fleet its situational awareness. This is a hands-on role that spans the full problem: the models that let our robots detect pallet trays and other bots and estimate distance to them, the simulation and data pipelines that produce and validate those models, and the integration that gets perception onto the robot and into the hands of the motion and safety systems. Perception feeds everything downstream: collision avoidance, localization, and safety all depend on how well our robots see and interpret their environment, so this role has real impact across the robotics stack. You'll own significant perception workstreams end to end, from data collection through deployment, and work closely with the camera, robotics, and safety teams. What you'll do: * Build and improve perception models for the fleet, including object detection (pallet trays and bots), depth estimation (using both learned and geometric methods such as projection geometry and calibration), and scene understanding that power collision avoidance and other on-bot behaviors. * Own the vision data engine end to end: ETL pipelines that ingest on-bot and rollbot logs, route data through cloud storage and labeling workflows, and produce curated, training-ready datasets on demand, so improvements are measurable and reproducible. * Advance simulation and sim-to-real: train and evaluate models in simulation, validate them in software-in-the-loop, and systematically close the gap to real robot data. * Develop camera and time-of-flight based localization that feeds the safety system, improving the robustness of fleet localization. * Deploy and integrate perception on the robot: optimize models for on-bot compute, publish outputs to downstream consumers, implement consumer handling of perception inputs and build the automated processes to release and monitor models across the fleet. * Contribute technical leadership: author design docs, review code, mentor engineers, and help shape the perception roadmap in partnership with the camera/sensing, robotics, and safety teams. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Robots are coming into the wild! Full-Stack Robotics Engineers, be ready!](https://www.wearedevelopers.com/videos/479-robots-are-coming-into-the-wild-full-stack-robotics-engineers-be-ready) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Perception to Autonomy: Building Agentic Edge AI Robots with ROS 2](https://www.wearedevelopers.com/videos/100295-from-perception-to-autonomy-building-agentic-edge-ai-robots-with-ros-2) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)