Jobs at Bedrock Robotics

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.

As a Machine Learning Engineer, you will design and train perception models for robotics, focusing on 3D perception systems. You will tackle complex physical challenges, optimize models for embedded hardware, and collaborate with cross-functional teams.

Develop and scale learning models for behavior cloning and reinforcement learning. Build data pipelines and evaluate model performance for integration into autonomous systems.

The Site Operations Manager will lead the deployment of autonomous construction technology, ensuring site launches, operational performance, and strong partner relationships throughout project lifecycles.

The Hardware Engineer will design, integrate, and validate hardware for autonomous machines, working on system architecture and collaborating with engineers and vendors.

Architect and maintain the embedded Linux stack for autonomous systems, optimize performance, and lead cross-functional technical efforts.

Lead cross-functional programs that deliver cloud infrastructure, data platforms, and user-facing applications for autonomous construction systems. Align engineering, commercial, and operations workstreams, mitigate technical and program risk, shape roadmaps and lightweight PM practices, and drive safety-critical validation for physical deployments.