Jobs at Bedrock Robotics

Own and evolve the developer platform: design and operate CI/CD, monorepo tooling, GitHub automation, and self-service developer workflows to improve build reliability, speed, and engineering productivity across the org.

The Robotics Application Engineer leads deployment of autonomous excavators, conducts site evaluations, troubleshoots issues, collaborates on technical strategy, and communicates value to customers.

Lead design and build core backend services and infrastructure for autonomous construction equipment, including fleet management, task orchestration, monitoring, tooling for testing and deployments, and collaboration with frontend, ML, and robotics teams.

Build and productionize AI-driven annotation systems and agentic workflows for robot sensor data: auto-labelers, orchestration, quality harnesses, versioning, metrics, and integrations to support perception and autonomy.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Lead end-to-end product quality across hardware development, manufacturing, supplier quality, reliability testing, and deployed robotic fleets. Establish IQC/IPQC/OQC, quality gates, FMEA and CAPA processes, define validation and test strategies (HALT/HASS, HIL/SIL), drive root-cause analysis, KPIs (yield, DPPM, MTBF/MTTR), and build automated diagnostics and AI-enabled workflows to improve reliability and field uptime.

The Frontend Engineer will establish UI interactions, architecture, and workflows, working with complex data and operating in a fast-paced startup environment. Responsibilities include designing frontend systems, optimizing performance, and collaborating with engineers across disciplines.

Build and scale high-fidelity simulation environments for autonomous construction machines. Develop simulation systems to support ML-based autonomy, improve realism and performance (including terrain deformation), guide scene generation and neural rendering work, and produce production-quality simulation code and content to validate autonomy and safety at scale.

The Technical Program Manager will manage execution across departments, anticipate risks, shape roadmaps, and drive critical initiatives to expand the deployment of autonomous construction machinery.

Define, develop, and execute end-to-end test plans for robotic hardware and control software. Design test fixtures, run functional/performance/environmental tests, analyze data, track defects, and coordinate integration testing with development and operations to ensure deployment readiness.

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.

Develop and optimize sensor pipelines for autonomous construction machines, involving sensor calibration, validation, and data analysis in real-world conditions.

Design, build, and scale automated hardware test systems and diagnostics across NPI and volume manufacturing. Own test strategy, frameworks, MES integration, failure analysis, reliability testing, data collection/analytics, and operator training to improve yield and reduce field escapes.

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.

Owner of corporate infrastructure projects: design and deploy identity, endpoints, networking, and SaaS integrations. Consult with business teams, write documentation/RFDs, modify IaC (HCL), script automations, define workplace tech standards for multi-site rollouts, and coordinate vendors from kickoff to handoff.

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.

Join a cutting-edge team deploying autonomous systems on construction machinery, enhancing safety and project efficiency in infrastructure projects.

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