Software Engineer, Cloud Infrastructure
As a Senior AI Research Scientist for Vision-guided robotics, you will lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. You will explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Your work will involve creating models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. You will collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities that can be deployed on physical hardware for industrial applications. Responsibilities also include researching and developing deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios, designing algorithms for precise manipulation of complex or deformable objects, collaborating with software engineers to optimize and deploy research prototypes onto physical robotic hardware, evaluating model performance in simulation and real-world environments, identifying opportunities to apply advancements in computer vision and robot learning to industrial problems, mentoring junior researchers, and contributing to the technical direction of the manipulation research roadmap.
Robotics Software Engineer, Robot Control and Hardware Integration
As a Senior AI Research Scientist for Vision-guided robotics, the role involves leading the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Responsibilities include exploring the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments, creating models that integrate visual data to guide physical manipulation beyond simple grasping to sophisticated handling of diverse items. The role also entails collaborating with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Tasks also include researching and developing deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios; designing algorithms for high precision manipulation of complex or deformable objects; collaborating with software engineers to optimize and deploy research prototypes onto physical robotic hardware; evaluating model performance in simulation and real-world environments to ensure robustness; identifying opportunities to apply state-of-the-art advancements in computer vision and robot learning to industrial problems; mentoring junior researchers; and contributing to the technical direction of the manipulation research roadmap.
General Software Engineer
Design, develop, test, and deploy robust and scalable software solutions for counter-drone systems. Collaborate with cross-functional teams, including hardware engineers, data scientists, and subject matter experts, to define system requirements and architecture. Implement algorithms for drone detection, tracking, identification, and mitigation. Optimize software for real-time performance and resource efficiency in embedded and distributed environments. Participate in code reviews, contribute to architectural discussions, and maintain high code quality standards. Troubleshoot and debug software issues, ensuring system reliability and performance. Stay up-to-date with emerging technologies and industry best practices in software development and counter-drone capabilities.
Multi‑Target Tracking & Sensor Fusion Engineer (R4172)
Design, research, and implement state-of-the-art multi-target tracking and data association algorithms. Develop production-quality C++ software for deployed military aviation platforms, ensuring deterministic, real-time performance. Build and maintain comprehensive unit, integration, and system-level tests to validate algorithm correctness and robustness. Enhance and calibrate sensor models in advanced simulation and hardware-in-the-loop (HWIL) environments. Collaborate on feature planning, decomposition, and milestone execution within an agile development framework. Contribute to flight-test planning, performance analysis, benchmarking, and regression evaluation. For principal-level applicants, provide technical leadership, design reviews, algorithmic mentorship, and subject-matter expertise across the autonomy organization.
Robot Network Software Engineer
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
RTL & Codesign Engineer
Design and implement key compute, memory, and interconnect components for a custom AI accelerator by producing clean, production-quality microarchitecture and RTL for major accelerator subsystems. Contribute to architectural studies, including performance modeling and feasibility analysis. Collaborate with software, simulator, and compiler teams to ensure hardware/software co-design and workload fit. Partner with design verification (DV) and physical design (PD) teams to ensure functional correctness, timing closure, area/power targets, and clean integration. Build and review performance and functional models to validate design intent. Participate in design reviews, documentation, and bring-up support across the full silicon lifecycle.
Validation Engineer – Mechanical Systems
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
Scale.ai - Software Engineer (Robotics & Autonomous Systems)
In this role, you will own and architect large-scale data processing pipelines for robotics and autonomous vehicle datasets. You will build machine learning training and fine-tuning pipelines using Scale's robotics data. The job requires working across backend technologies (Python, Node.js, C++) and frontend stacks (React, TypeScript) to build end-to-end solutions. You will develop tools and systems for robotics data collection, teleoperation, and model evaluation, and interact directly with robotics and autonomous vehicle stakeholders to understand their technical needs and drive product development. Responsibilities also include building real-time systems for robotic control, sensor fusion, and perception pipelines, designing comprehensive monitoring and evaluation frameworks for robotics models and data quality, collaborating with machine learning engineers and researchers to bring robotics research into production, and delivering features at high velocity while maintaining system reliability and performance.
Senior Engineering Manager, Deep Learning
Oversee the entire perception system development life cycle, from problem definition to deployment and ongoing improvement. Lead a team of computer vision and perception engineers to develop and refine the system in a hands-on manner. Spearhead the development of robust computer vision algorithms for object detection, tracking, semantic segmentation, and classification. Champion the development and training of deep learning models for complex urban scene perception and real-time analysis. Collaborate with cross-functional teams for seamless integration and monitoring of perception models. Analyze data to identify performance bottlenecks and opportunities for enhancing the perception system. Foster automation in the improvement cycles of deep learning models used within the perception system. Communicate technical findings and insights effectively to stakeholders across the company to drive performance improvements. Utilize data visualization tools to present complex information clearly for informed decision-making.
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