C++ AI Jobs

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As a Software Engineer on Consumer Products Research, the responsibilities include building and shipping production software for sensing algorithms by translating algorithm prototypes into reliable end-to-end systems, implementing and owning key parts of the Python shipping pipeline including integration surfaces, evaluation hooks, and quality/performance guardrails. The role also involves developing embedded/on-device software in an RTOS environment (such as Zephyr) and deploying models to device runtimes and hardware accelerators. Additional responsibilities include optimizing real-time on-device perception loops for stability, latency, power, and memory constraints, creating data collection and instrumentation tooling to bring up new sensing modalities and accelerate iteration from prototype to dataset to model to device, and partnering cross-functionally with algorithms, human data, firmware/hardware teams to debug, profile, and harden systems against real-world variability.

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Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency. Build tooling and establish development best practices to manage and upgrade foundational libraries such as Nvidia driver, PyTorch, TensorRT, to improve ML developer experience and expedite debugging efforts. Collaborate closely with cross-functional teams including applied ML research, high-performance compute, advanced hardware engineering, and data science to define requirements and align on architectural decisions. Work across multiple ML teams within Zoox, supporting in- and off-vehicle ML use cases and coordinating to meet the needs of vehicle and ML teams to reduce the time from ideation to productionization of AI innovations.

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You will develop ML/AI that leverage and extend the latest state-of-the-art methods and architectures, design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios, work on impactful projects, and collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in production systems.

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As a Senior Software Engineer on the Pilots team, the responsibilities include delivering robust, thoroughly tested, and maintainable C++ code for edge and robotics platforms, designing, implementing, and owning prototype perception systems that may transition into production-grade solutions, constructing and refining real-time perception pipelines including detection, tracking, and sensor fusion, adapting and integrating ML and CV models for Hayden-specific applications, driving technical decision-making balancing prototyping speed with production readiness, collaborating with the Product team and cross-functional Engineering departments, and contributing to shared infrastructure, tooling, and architectural patterns as pilots mature into foundational products.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control by designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms for high precision manipulation of complex or deformable objects. Work with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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The Computer Vision Engineer is responsible for developing the front-end of the visual inertial odometry (VIO) algorithmic stack, including matching between frames and stereo pairs, calibration of camera intrinsic and extrinsic parameters, and detection of obstruction. They will implement and optimize the algorithmic stack for embedded platforms, conduct testing, validation, and monitoring of algorithms in simulation and real-world environments, and develop inspection and monitoring tools. The role also involves cross-team collaboration, working closely with system engineers, optical engineers, and software engineers, and effectively communicating findings to stakeholders.

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The Site Reliability Engineer is responsible for designing and operating reliable managed AI services focused on serving and scaling large language model workloads. They build automation and reliability tooling to support distributed AI pipelines and inference services, define, measure, and improve SLIs/SLOs across AI workloads to ensure performance and reliability, and collaborate with AI, platform, and infrastructure teams to optimize large-scale training and inference clusters. Additionally, they automate observability by building telemetry and performance tuning strategies for latency-sensitive AI services, investigate and resolve reliability issues in distributed AI systems using telemetry, logs, and profiling, and contribute to the architecture of next-generation distributed systems designed specifically for AI-first environments.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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As a software engineer on the perception mapping team at Zoox, you will curate, validate, and label datasets for model training and validation. You will research, implement, and train machine learning models to perform semantic map element detection and closely collaborate with validation teams to formulate and execute model validation pipelines. You will integrate models into the greater onboard autonomy system within compute budgets. Additionally, you will serve as a technical leader on the team, maintaining coding and ML development best practices and contributing to architectural decisions.