Jobs at Genesis

Lead customer robot deployments from workflow discovery and site assessment through scoping, financial modeling, technical alignment, execution, acceptance testing, training, and handoff. Partner with engineering, research, safety, solution engineering, customers, and external teams to define requirements, success metrics, dependencies, and milestones. Translate field feedback into deployment practices while managing scope changes, implementation tradeoffs, customer value, utilization, and support needs.

Owns the technical path from customer use cases to reliable robot deployments. Responsibilities include assessing automation feasibility, defining requirements and success metrics, designing integrations across robotics hardware, software, tooling, and customer systems, configuring and debugging systems onsite, and improving deployment tools and playbooks. The role partners with research and engineering teams to resolve field failures and requires customer communication, hands-on troubleshooting, and travel to deployment sites.

Build and manage a production supplier base for robotic hardware, including sourcing, qualification, dual sourcing, commercial negotiations, capacity planning, BOM and cost control, supplier quality, logistics, and MRP systems. Ensure clear-to-build readiness, inventory accuracy, supplier performance, and production support while developing domestic and international suppliers and leading cross-functional collaboration with engineering, manufacturing, quality, legal, and finance.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Build and maintain the software engineering backbone of Genesis-World, an open-source multi-physics robotics simulation platform. Responsibilities include scalable testing for JIT-compiled systems, deterministic simulation replay, autodiff integration, scene editing, plugin architecture, actuation, local time stepping, event systems, serialization, telemetry, performance improvements, and legacy refactoring. The role also involves code reviews, issue triage, contributor support, and collaboration with compiler and physics teams across GPU and operating-system platforms.

Develop and ship production-ready physics simulation capabilities for Genesis-World, improving speed, fidelity, completeness, versatility, and scalability. Implement and optimize multi-physics algorithms, constraint solvers, adaptive timesteps, contact resolution, and heterogeneous solver coupling using Python and GPU/CPU backends. Validate methods through analytical solutions, cross-engine comparisons, and real-world data, while delivering tested, documented code for robotics learning pipelines.

Own Quadrants, an open-source Python compiler for performance-critical numerical computing and physics simulation. Lead its API, intermediate representation, lowering, compiler backends, optimization passes, numerical primitives, benchmarks, roadmap, and user adoption. Optimize GPU and CPU execution, maintain system coherence, track regressions, improve usability, and collaborate with Genesis users. The role combines compiler research, GPU engineering, API design, open-source product ownership, and future leadership of a small team.

Design, build, and scale security infrastructure across systems, networks, endpoints, and products. Own security architecture, detection/response, vulnerability and risk management, and security policies. Integrate security into the SDLC, partner with engineering, run incident response and monitoring, lead security education, and temporarily own IT infrastructure.

Build and optimize low-latency on-device and distributed GPU inference pipelines. Implement and tune low-level CUDA/Triton kernels, optimize for throughput and latency, and develop monitoring/debugging tools to ensure reliable, deterministic inference in robotics and cluster environments.

Own customer-facing go-to-market for robotics: identify design partners, run pilots to production, close technical commercial deals, and feed customer insights into product and engineering. Lead deployments, build playbooks, define pricing, and align partnerships to scale early-adopter engagements.

Hands-on field engineer who keeps robot data collection running: stand up and maintain stations, triage and resolve hardware/software/data issues on customer and partner sites, bring up new tasks and sites, and feed operational insights back to model, platform, and hardware teams.

Lead development of a high-performance robotics simulation platform and its compiler stack. Design compute infrastructure and data flow for physics simulation and model training, optimize GPU codegen and JIT compilation, improve differentiable programming support, and contribute to open-source ML compiler projects while collaborating with simulation and robotics engineers.

Own and scale end-to-end physical-data collection operations: scheduling, operator utilization, station uptime, throughput and quality. Recruit and develop operators and shift leads, standardize playbooks and SOPs across sites, partner with Quality and Annotation teams, stand up partner-site deployments, and build reporting that tracks collected hours, quality, and cost.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Design and maintain foundational platform frameworks and internal tooling to accelerate AI research. Collaborate with cross-functional engineering and research teams to build modular, reusable systems, improve workflows, ensure high code quality, and implement organization-wide, low-disruption changes.

Design, prototype, and build robotic hardware systems; develop scalable data-collection platforms; improve simulation fidelity via system identification; maintain in-house hardware infrastructure; collaborate with engineering and simulation teams to deliver robust, scalable physical AI systems.

Own and scale the pipeline that converts raw robot demonstrations (trajectory/video) into training-ready datasets. Design ontologies, run and automate annotation operations (internal and vendor), apply vision-language models for labeling and synthesis, track quality metrics, and close the loop from eval failures to targeted data collection to improve the model.

Develop, optimize, and deploy learning-based robotic manipulation and locomotion systems. Build low-latency teleoperation, train policies with reinforcement and imitation learning, diagnose control-pipeline bottlenecks, and minimize sim-to-real gaps through co-optimization of simulation and real robot behavior while collaborating with a cross-functional team.

Design, build, and maintain petabyte-scale batch and streaming data pipelines and core data infrastructure for robotics foundation model training. Own data models, storage, ingestion, transformation, and orchestration. Standardize pipelines across teleoperation and simulation datasets and collaborate with cross-functional team to support Physical AI development.

Lead the product roadmap for Physical AI, translating research into deployable robotics/autonomy products. Define readiness, success metrics, and reliability thresholds. Align research, engineering, and field teams, prioritize field validation over benchmarks, write clear specs and decision logs, and operate in ambiguous, fast-moving environments.