Top Tech Jobs & Startup Jobs

Develop and debug embedded Linux firmware for camera and sensor recording systems. Responsibilities include board bring-up, reliable data paths, performance and power optimization, recovery from interruptions, field-failure reproduction, and validation across hardware configurations. The role requires strong C or C++ skills, Python or shell scripting, Linux systems knowledge, embedded ARM experience, concurrency and resource profiling expertise, and experience creating reproducible releases and realistic hardware tests.

Leads end-to-end development of hardware systems for robotics and embodied AI, from product definition and architecture through prototyping, validation, manufacturing, and deployment. The role requires hands-on expertise across hardware disciplines, system integration, experimentation, debugging, supplier coordination, and technical leadership.

Designs and validates electronics for robotics data-capture hardware, including schematics, PCB layouts, sensor interfaces, board bring-up, bench debugging, and system validation. Collaborates with firmware, mechanical engineering, and suppliers to improve prototypes, production designs, component qualification, and field performance. Documents measurements and design changes while addressing signal quality, timing, power consumption, and hardware-firmware integration.

Build native Android applications that connect capture hardware, guide data recording, display status, and support recovery from interruptions. Optimize video performance, memory, battery usage, responsiveness, and connectivity across Android devices. Develop diagnostics and automated workflow tests while collaborating with firmware engineers to reproduce and resolve hardware, transfer, and device-interface issues.

Build and integrate robotic hardware and software for repeatable experiments, data collection, demonstrations, and learned-policy deployment. Develop calibration and diagnostic tools for coordinate frames, timing, and sensor quality. Create recording, teleoperation, and evaluation workflows while debugging failures across software, electronics, sensors, and mechanics. Improve system reliability through software enhancements and recovery procedures.

Train and evaluate computer vision models for embedded deployment, build efficient C++ inference pipelines, and optimize latency, memory, power, and sustained performance. Integrate models with embedded Linux, test on representative hardware and data, and investigate errors across preprocessing, inference, and system integration. Lead candidates will set architecture and evaluation standards while guiding model and software development.

Develop manufacturing processes, production-readiness and capacity plans for electronic capture hardware. Build and qualify fixtures, calibration equipment, and production tests; analyze yield, cycle-time, and failure data; improve designs for manufacturing; establish repair and rework processes; and guide suppliers through qualification, technical reviews, quality improvements, and production changes.

Build and launch 0-to-1 products for robotics, manufacturing, science, and AI-focused labs. Develop end-to-end frontend, backend, API, and data workflows; integrate external systems; create reusable components; and own testing, deployment, monitoring, reliability, and support. Work directly with users to understand workflows, identify bottlenecks, define useful releases, and iterate based on production evidence. The role requires strong product judgment, adaptability, and ownership across unfamiliar technical domains.

Build reinforcement learning environments for robotics and embodied AI research. Define tasks, observations, actions, rewards, transitions, termination conditions, and evaluation metrics. Train baseline agents, run controlled experiments, assess generalization and uncertainty, investigate failures, and identify data or environment issues. Collaborate with domain experts and engineers to develop reusable, documented research systems and benchmarks.

Build scalable physics-based simulation environments and world models for robotics and embodied AI. Develop parallel training and evaluation pipelines, calibrate simulations against real-world data, improve sim-to-real transfer, and create learned or hybrid dynamics models for prediction, planning, and control. The role requires strong simulation, robotics learning, Python, and C++ expertise, along with experimental judgment and experience building reproducible research infrastructure.