AI Autonomous Systems Engineer Jobs

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Develop state-of-the-art navigation and sensor fusion algorithms for UAVs, design and implement GNC and flight control systems, build filtering and estimation strategies for robust and efficient flight performance, run extensive simulations including Monte Carlo, SITL, HITL, and coverage testing, analyze test flight data and refine algorithmic performance, support full-stack system integration including GNSS, INS/IMU, localization, and fusion, and maintain and evolve a flight-proven flight computer across multiple UAV platforms.

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The role involves defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications. The engineer will contribute to current datacenter networking efforts while helping to seed and specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingestion, with an initial focus on datacenter networking and robotics in the automotive/robotics space. The position requires working at the intersection of Network on Chip (NoC) design, performance modeling, and RTL design to guide architectural decisions and collaborating across hardware, software, and systems teams to define and refine networking requirements. The responsibilities also include driving forward next-generation CPU networking architecture for AI/ML workloads and taking early-stage automotive/robotics networking concepts from seeding and specification through to project initiation.

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The role involves defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications, contributing to current datacenter networking efforts, and helping to seed and specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingest. Responsibilities also include working at the intersection of Network-on-Chip (NoC), performance modeling, and RTL design to guide architectural decisions, collaborating across hardware, software, and systems teams to define and refine networking requirements, and driving forward next-generation CPU networking architecture for AI/ML workloads.

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As a Forward Deployed Engineer at Sunrise Robotics, you will deploy Sunrise robotic systems in live manufacturing environments to ensure successful customer go-lives. You will translate real-world production constraints into structured feedback that improves product capabilities and deployment workflows, identify opportunities to reduce non-recurring engineering effort and improve delivery scalability, and contribute to refining tools, processes, and system architectures to enable repeatable deployments. Collaboration with AI, robotics, product, and commercial teams is essential to align customer needs with product evolution. You will support pilot launches and early deployments, ensuring systems meet performance and operational success criteria, act as a technical partner to customers during integration to build trust and ensure long-term success, provide technical support during troubleshooting and maintenance, and investigate operational incidents or anomalies to ensure timely resolution and system reliability.

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Contribute to defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications, including current datacenter networking and specifying future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingest. Work at the intersection of Network on Chip (NoC), performance modeling, and RTL design to guide architectural decisions. Collaborate across hardware, software, and systems teams to define and refine networking requirements. Help drive the advancement of next-generation CPU networking architecture for AI/ML workloads.

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The Field Application Engineer, Automotive Robotics will contribute to defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications, including both datacenter networking efforts and early-stage automotive/robotics scoping and specifications. Responsibilities include working at the intersection of NoC, performance modeling, and RTL design to guide architectural decisions, collaborating across hardware, software, and systems teams to define and refine networking requirements, and helping to drive forward next-generation CPU networking architecture for AI/ML workloads. The role involves taking an early-stage concept within automotive/robotics networking from seeding and specification through to project initiation.

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The job involves defining and building next-generation CPU networking architecture for both datacenter and emerging robotics/automotive applications. Responsibilities include contributing to current datacenter networking efforts and helping to specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingest, with an initial focus on datacenter networking and robotics within the automotive/robotics space. The role requires working at the intersection of Network on Chip (NoC), performance modeling, and RTL design to guide architectural decisions, collaborating across hardware, software, and systems teams to define and refine networking requirements, and helping to drive forward next-generation CPU networking architecture for AI/ML workloads. Additionally, the position involves taking early-stage automotive/robotics networking concepts from seeding and specification through to project initiation.

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As an Automotive and Robotics SoC Architect at Tenstorrent, you will define scalable, top-down system architectures that unify the company's CPU and AI technologies for next-generation automotive applications. This role involves shaping the architectural direction of automotive and robotics products to meet high standards for performance, safety, reliability, and security. The position requires strong technical leadership, systems thinking, and cross-functional collaboration, and is central to delivering world-class automotive solutions.

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The Systems Engineer is responsible for owning the holistic performance of Sesame’s wearable devices across the full stack, including hardware selection and integration, firmware, signal processing, and application behavior. They research, evaluate, and recommend optimal sensor technologies and devices for various wearable applications considering physical, electrical, software capabilities, cost, schedule, and user impact. They own the end-to-end performance of sensor systems from prototyping through mass production, managing latency, power consumption, thermal constraints, and reliability. The role includes defining system-level test plans, acceptance criteria, and specifications to ensure high-quality user experience and validating each layer of the stack. They design and supervise data collection strategies for ground-truth data sets needed for algorithm and model development. Additionally, they develop, test, and implement signal processing, sensor fusion, and calibration systems to translate raw sensor data into usable outputs, and collaborate with the ML team to enhance Sesame agents’ responses by integrating sensor data.

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As a Senior Autonomy Systems Test Engineer, you will accelerate product development by helping developers build safe and reliable autonomous driving software. You will oversee the development of extensive test plans, develop standardized simulated test design tooling processes to execute various scenarios, validate end-to-end behaviors, and create triage pipelines for analyzing issues seen during offline and on-vehicle testing. You will participate regularly in in-vehicle testing missions to observe feature behavior in real-world conditions, help triage and root cause issues seen during testing, analyze test results to ensure no regression in existing functionality, and update testing processes for scaling QA efficiency. Your specific duties include creating test strategies and test plans for self-driving behavior features, identifying, tracking, reporting, and resolving test strategy, planning, or implementation issues with cross-functional software teams, proposing and validating new testing methodology for the AI stack using automated metrics, designing, developing, and executing synthetic and log-based test scenarios on an in-house simulation test framework, and compiling triage strategies and results from different QA validation platforms and pipelines.