Jobs at Humble Robotics

Own electrical architecture and PCB design for autonomous vehicle hardware, including schematic capture, layout, prototype bring-up, debugging, validation, EMC, functional safety, and production transition. Lead component selection, fab and assembly engagement, DFM/DFT reviews, build documentation, design reviews, and root-cause analysis. Partner with firmware, autonomy, and systems teams to resolve end-to-end issues and establish hardware bring-up and validation best practices.

Own corporate IT across identity, access, endpoints, networks, SaaS, lab infrastructure, and robotics support. Manage onboarding, device lifecycle, networking, ticketing, procurement, asset inventory, conference-room technology, and endpoint security. Automate repetitive processes with Bash, Python, Ansible, and APIs while supporting engineering workstations, servers, storage, and vehicle data workflows. The role is primarily onsite in San Francisco, includes occasional after-hours on-call participation as the team grows, and requires working in lab and shop-floor environments.

Own the accounting function end to end, including monthly close, US GAAP financial reporting, reconciliations, audit leadership, technical accounting, controls, AP, payroll, equity accounting, tax compliance, and systems integration. Build a scalable accounting operation using automation and AI, improve close processes, manage external advisors, and support budgeting, forecasting, modeling, and variance analysis while developing the future accounting team.

Build data systems for autonomous vehicle development, including sensor-data ingestion, validation, processing, enrichment, dataset tooling, schema migrations, backfills, and workflow reliability. Collaborate with machine learning and vehicle software engineers to create datasets for model training, evaluation, and debugging. Investigate data quality and performance issues while improving autonomy data workflows.

Develop and test safety-critical, real-time embedded firmware in C++ for automotive microcontrollers and vehicle systems. Build communication interfaces, support board bring-up, collaborate on hardware-software debugging, troubleshoot board- and vehicle-level issues, and assist with manufacturing tests and vehicle software deployment.

Electrical Engineering Intern supporting autonomous truck hardware programs. Responsibilities include designing test boards in Altium, assembling and reworking PCBs, integrating hardware on benches and vehicles, updating schematics and BOMs, conducting electrical tests, automating testing with Python or other languages, and documenting results. Candidates must be enrolled in a relevant engineering degree program, have Altium and hands-on electronics experience, understand circuit theory, and be eligible to work in the United States.

Build and maintain hardware integration test benches, bring up new vehicle hardware, troubleshoot electrical issues, and support autonomous vehicle fleet validation. The role includes writing Python automation scripts, building and repairing harnesses and electrical boards, conducting hi-pot testing, collaborating with engineering teams and suppliers, maintaining documentation, and following high-voltage safety procedures.

Design, build, and scale vehicle data-collection networking and infrastructure. Integrate software with onboard compute, sensors, and navigation. Build CI/CD, ensure performance, security, and operational excellence for an autonomous vehicle fleet.

Build and optimize real-time on-vehicle software for autonomous trucks, including data logging, cross-compilation pipelines, performance instrumentation, testing/CI, and integrations with ML, firmware, cloud, and fleet systems.

Own system-level requirements and architecture for an autonomous vehicle platform. Decompose vehicle requirements into subsystem/component requirements, run design reviews, drive verification/validation, partner with Mechanical, SW/Autonomy, and Functional Safety teams, and lead root-cause analysis and requirement-based improvements.

Lead end-to-end design and delivery of the electric powertrain system (battery, HV distribution, motors, inverters, drivetrain). Define system architecture, perform system-level modeling and validation, integrate battery/BMS and thermal strategies, oversee prototype builds and testing, manage suppliers, and ensure HV safety, durability, and production readiness.

Design, build, and scale ML infrastructure: ingest vehicle sensor data, create batch pipelines for dataset curation, run distributed GPU training, and ensure performance, observability, efficiency, and security across the ML pipeline while partnering with ML teams.

Design, prototype, and validate mechanical systems for large autonomous vehicles. Create CAD models and drawings, build and test hardware in-shop, integrate sensors and subsystems, manage sourcing and vendors, perform root cause analysis, and support field testing and deployments.

Develop and maintain safety-critical, real-time embedded firmware for automotive ECUs. Work across board bring-up to production: bootloaders, drivers, RTOS configuration, automotive communications, hardware-software debugging, testing (unit, integration, HIL), manufacturing test support, and field issue root-cause analysis.

Design and bring up electronic hardware for autonomous vehicles end-to-end: schematic/layout with Altium, PCB bring-up and debugging, integrate sensors/compute/actuators, run lab tests with scopes and logic analyzers, collaborate with Mechanical, Firmware, and Systems engineers, and document test results and issues.

Build and maintain Python-based simulation pipelines and tooling for closed-loop testing, synthetic data generation, and sensor simulation. Integrate neural rendering methods (Gaussian splatting, NeRFs, world models) into the sensor stack, expand scenario libraries and benchmarks, and collaborate with ML/autonomy teams to reduce sim-to-real gaps and support model training.

Build end-to-end ML software for autonomy foundation models: data pipelines, training/evaluation workflows, model implementation (LLM/VLM/VLA), simulator integration, and low-latency serving and inference tooling for vehicle deployment.