AI Robotics Software Engineer Jobs

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In this role, you will work closely with customers to understand their requirements, provide technical expertise and customer support during deployment, and ensure successful integration of Hivemind. You will deploy with customers on site, approximately 50% travel, to support software integration and development activities. Become an expert user of the Hivemind enterprise software stack and its various autonomy modules. Provide technical support and training to customers on use of Hivemind. Develop AI & Autonomy applications using the Shield AI enterprise software development kit. Assist the sales team in pre-sales activities such as demos, conferences, and immersions. Assist in post-sales deployment and integration of Shield AI enterprise software products. Develop and maintain technical documentation and training materials. Help customers debug software/API integration issues. Collaborate with the product engineering team to address customer feedback and improve products.

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Design, build, and deploy robotic and embedded software that powers advanced pilot assistance systems in production environments. Own autonomy-related features or subsystems from concept through deployment, focusing on reliability and performance. Write, review, and maintain high-quality Python and C++ code across autonomy, systems, and embedded components. Integrate software with hardware, sensors, and perception or data ingestion pipelines to support autonomous and operator-in-the-loop decision-making. Optimize software for edge compute environments by managing CPU/GPU usage, latency, and implementing safety mechanisms and fail-safes. Lead testing, validation, and deployment efforts to meet safety-critical and mission-critical requirements. Mentor engineers and contribute to technical direction through design and code reviews and hands-on collaboration.

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Execute the PCP metrics pipeline to assess how hardware updates across various sensors affect PCP performance sensitivity in 3D scene understanding, live mapping, and localization, especially under adverse weather and winter conditions. Design and conduct structured A/B testing experiments, including side-by-side custom mount runs and tandem vehicle data collection, to clear sensors for winter ODD. Collaborate with Autonomous Hardware Engineering (AHE) to correlate sensor-specific lab tests with downstream PCP performance. Detect root cause regressions to understand compensation by early fusion models for modality degradation in challenging environments. Design and develop performant and efficient multi-sensor fusion models to leverage advantages of on-board low-cost sensors to improve Perception performance on highways and in adverse weather.

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As a Staff Autonomy Engineer at Chef Robotics, you will own the technical architecture of Chef's autonomy stack end-to-end including perception, action prediction, closed-loop control, and sensor fusion, from prototype to production deployment. You will design and implement learning-based systems for robot actions generalizing across diverse ingredient types, build closed-loop control systems integrating vision and various sensors for manipulation, and develop rapid onboarding capabilities for new meal types and ingredient configurations. You will instrument autonomy systems for production monitoring, failure detection, and continuous improvement through data collection pipelines. Additionally, you will define objective qualification criteria for autonomy subsystems, drive reliability and performance improvements using production data, investigate and integrate advances in robot learning and manipulation research, mentor senior engineers, lead design reviews, and help build a scalable team for Chef's autonomy stack.

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As a Senior Software Engineer on the Robotics Platform team at Chef Robotics, you will lead the upgrade of autonomy software OS and middleware stack across production systems. You will define software architecture standards, release processes, and testing practices for platform software. You are responsible for fleet-wide platform reliability by diagnosing and fixing low-level systems issues such as drivers, networking, and Linux configuration across deployed robots. You will investigate and resolve networking and load bottlenecks affecting perception and autonomy algorithm performance. You will build provisioning and configuration tooling to scale robot bring-up with manufacturing, integrate new hardware, sensors, and cameras into the platform, and monitor performance across the fleet. You will set the bar for engineering quality by producing well-structured, well-tested, production-ready code. The role requires working onsite five days a week with a high-ownership team, shipping hardware-software systems into customer facilities, and maintaining their operation with startup urgency.

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As a Senior Perception Engineer at Chef Robotics, you will own the full stack of how robots see and understand the world, including integrating camera hardware, training deep learning models, and ensuring real-time model performance on the factory floor. You will design, train, and optimize deep learning models for detection, segmentation, pose estimation, and classification, focusing on real-world robustness. You will build low-latency inference pipelines optimized for embedded and edge hardware, develop multi-object tracking algorithms, and solve perception problems specific to food robotics such as deformable objects and occlusions. You will manage the end-to-end machine learning lifecycle including data collection, annotation tooling, dataset curation, augmentation, model training, evaluation, deployment, and debugging in the field. Additionally, you will develop tooling to monitor model performance in production, partner with robotics, hardware, and software engineers to translate perception capabilities into robot behaviors, help define the perception roadmap, influence the team's technical direction, and assist in integrating new cameras and sensors for enhanced robotic vision.

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Serve as the design verification SME for semiconductor deployments, helping teams reason about verification workflows across block, subsystem, and SoC environments. Shape AI-assisted workflows for test generation, regression triage, debug, root-cause analysis, and coverage closure. Curate evaluations with FDEs and customer SMEs, including golden tasks, labeled examples, rubrics, acceptance criteria, and realistic benchmarks grounded in solved issues and real engineering workflows. Build lightweight prototypes, evaluation harnesses, and tooling that validate opportunities and improve solution quality. Educate and mentor the broader FDE team on verification concepts, tooling, and methodology so the organization can engage semiconductor workflows with greater depth and confidence. Partner with FDEs during customer discovery and scoping to translate ambiguous pain points into clear solution hypotheses, success criteria, and technical plans. Support customer-facing technical conversations as a trusted advisor, engaging credibly with technical leaders. Progressively take on broader Forward Deployed Engineering responsibilities, including customer discovery, solution architecture, prototype development, production deployment, and ownership of technical workstreams.

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Design, build, and deploy robotic and embedded software that powers advanced pilot assistance systems in production environments. Own autonomy-related features or subsystems from concept through deployment, focusing on reliability and performance. Write, review, and maintain high-quality Python and C++ code across autonomy, systems, and embedded components. Integrate software with hardware, sensors, and perception or data ingestion pipelines to support autonomous and operator-in-the-loop decision-making. Optimize software for edge compute environments by managing CPU/GPU usage, latency, and implementing safety mechanisms and fail-safes. Lead testing, validation, and deployment efforts to ensure systems meet safety-critical and mission-critical requirements. Mentor engineers and contribute to technical direction through design reviews, code reviews, and hands-on collaboration.

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Develop a high-throughput, GPU-based simulation pipeline primarily for rigid body simulation for robots to train robotics foundation models. Implement essential robotics features, including actuators, sensors, and controllers, in collaboration with the robotics team. Work with the policy training team to bridge the sim-to-real gap. Develop a real-to-sim pipeline in collaboration with the asset generation team. Collaborate with a team committed to building general-purpose Physical AI.

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Develop and optimize a learning-based robotic manipulation control stack. Design and maintain a teleoperation system with smooth, precise motion and low latency. Train robotic policies for manipulation and locomotion with reinforcement learning and imitation learning. Deploy robotic policies and diagnose latency or bottlenecks in the control pipeline. Analyze and minimize the sim-to-real gap by co-optimizing simulation and real-world robot behavior. Collaborate with a team of driven individuals committed to building general-purpose Physical AI.