Jobs at Bedrock Robotics

Support the design, testing, integration, characterization, and validation of autonomous construction-machine hardware. Evaluate components, write Python, C/C++, or Rust scripts for sensor-data analysis and test automation, conduct tests, collect data, and document results. The role involves embedded compute, autonomous sensors, hardware packaging, communication protocols, circuits, signals, mechanical design, and hands-on laboratory work under senior engineer guidance.

Supports field validation of autonomous excavators by developing test plans, running safe structured tests, checking instrumentation and data quality, triaging failures, building Python automation, improving procedures, and presenting results to engineering teams. The role is full-time outdoors at a Texas test site, working around heavy equipment and collaborating with autonomy, controls, hardware, operators, and field test specialists.

Train and evaluate learned manipulation policies for autonomous excavators using real fleet data and simulated rollouts. Explore diffusion and flow-matching architectures, conditioning, and hyperparameters; build evaluation pipelines for task success, smoothness, safety, and operator-likeness; investigate failure modes and sim-to-real issues; and contribute reproducible code while collaborating with behavior, controls, simulation, and autonomy teams.

Own an open product problem for autonomous construction equipment, from framing and data gathering through prototyping and recommendations. Analyze fleet performance data, build tools and workflows with AI coding agents, gather requirements at field sites, collaborate across technical and commercial teams, and present findings to leadership. The role requires hands-on quantitative work, concise communication, and comfort working around heavy equipment.

Research and implement neural rendering methods such as NeRF and 3D Gaussian splatting for autonomous construction simulation. Train models on real-world fleet data, integrate rendered outputs into simulation pipelines, develop visual fidelity and autonomy-transfer metrics, and address challenges including dynamic objects, deformable terrain, dust, and harsh lighting. Document findings and recommend where neural rendering should replace traditional simulation assets.

Develop learned SLAM, mapping, localization, and semantic 3D mapping methods for autonomous excavators. Train models on fleet lidar and camera data, evaluate performance against classical baselines and ground truth, and prototype solutions for changing terrain, dust, occlusion, and sparse sensor data. Collaborate with perception and planning teams to deliver documented experiments and recommendations.

Develop and deploy production state-estimation and localization software for autonomous construction machines. Combine GNSS, IMU, lidar, and machine-sensor data; improve reliability through monitoring, fault detection, confidence estimation, and graceful degradation. Build ground-truth systems, offline estimators, metrics, and regression tests. Diagnose issues using field data, replay, simulation, and physical-machine testing while collaborating across controls, perception, safety, hardware, and systems teams.

Design and integrate hardware interfaces for autonomous heavy equipment, including brackets, enclosures, wiring, sensors, and compute systems. Build test stands, conduct lab validation, capture data, iterate on designs, fabricate and install components, document procedures, and collaborate with field teams. The role requires hands-on shop skills, CAD proficiency, basic electrical knowledge, and comfort working around heavy equipment in PPE.

Support the design, integration, characterization, testing, and validation of autonomous hardware systems for construction equipment. Evaluate components, write Python, C++, C, or Rust scripts for sensor data analysis and test automation, run tests, collect data, and document results. The intern will work with embedded compute platforms, autonomous sensors, communication protocols, and hardware integration under senior engineer guidance.

Sensor Systems Engineering Intern responsible for selecting and placing sensors on autonomous heavy construction equipment. Analyzes sensor coverage, occlusion, blind spots, field of view, range, and mounting constraints; develops geometry and coverage tools; conducts performance, cost, durability, and integration trade studies; prototypes mounting concepts; validates configurations on real machines; and translates findings into hardware requirements while collaborating with perception, hardware, and safety teams.

Integrate and optimize LLM and VLA models within Bedrock’s Rust-based onboard infrastructure for autonomous heavy machinery. Responsibilities include profiling inference performance, optimizing models with TensorRT and edge runtimes, streaming camera and LiDAR data, eliminating memory, compute, and IPC bottlenecks, and validating improvements on autonomous equipment. The role requires systems programming, GPU and CUDA knowledge, real-time systems expertise, and experience with PyTorch or JAX.

Train and evaluate world models using real autonomous construction-machine fleet data. Explore model architectures, conduct ablations, build evaluation metrics and harnesses, and collaborate with behavior and controls teams to connect model outputs to planning and control on excavators. Present findings and establish reliable foundations for future development.

Test and validate camera, lidar, and supporting sensor hardware for autonomous excavators. Responsibilities include building test fixtures, integrating wiring and DAQ systems, running lab and field test campaigns, automating data collection with Python, processing sensor logs, debugging electrical and mechanical hardware, selecting test equipment, documenting results, and presenting conclusions that guide sensor and system design.

Define and maintain system-level architecture and safety requirements for autonomous construction equipment. Lead functional safety concepts, HARA, FMEA, STPA, and FTA analyses; establish safety mechanisms, verification and validation plans, and requirements traceability. Collaborate across hardware, software, and mechanical teams, support design reviews and root cause analysis, and make technical tradeoffs affecting system performance and safety.

Lead full-cycle recruiting for AI/ML, perception, robotics, and technical leadership roles. Partner with engineering leaders and founders on hiring strategy, source specialized talent through research and technical networks, negotiate competitive offers, and manage candidate closing. Build AI-powered recruiting tools, maintain pipeline data, represent the company at conferences, strengthen employer branding, scale recruiting operations, and mentor future recruiters.

Lead data science across autonomy, safety, and product by analyzing machine telemetry and operational data, defining metrics, designing experiments, and partnering with ML platform and engineering to deliver insights that improve system performance and safety.

Lead and scale all accounting operations (GL, revenue recognition, AP/AR, payroll, consolidations, equity), drive month-end close, oversee fintech revenue streams, build internal controls and systems, partner with FP&A and product/engineering to automate financial workflows, support audits, SOX readiness, and board/investor reporting.

Design and implement onboard control algorithms and system models for heavy construction robots, develop MPC and RL-based controllers, perform system identification and simulation of multi-DOF arms and vehicle dynamics, and ensure safety-critical real-time embedded performance.

Drive hardware reliability for autonomous construction machines: define reliability strategy and requirements, lead DFMEAs and FTAs, build reliability models and tests (HALT/HASS, ALT), run FRACAS and root-cause investigations, qualify suppliers, and convert field failures into design and process improvements.

Develop and deploy perception algorithms and analytics for re-identification and productivity/safety metrics on large construction sites. Integrate raw sensor data (camera, lidar, IMU), adapt perception signals for customer-facing analytics, work with customers to scope capabilities, and deploy models to fleets of machines while designing evaluations and analyzing sensor data.