Jobs at Genesis

Source and engage elite research scientists, software engineers, and robotics/infra talent using targeted channels (papers, GitHub, conferences). Build high-converting outreach, map markets, maintain pipelines, and operationalize scalable sourcing (Ashby, automation, enrichment). Measure response rates and pipeline health to iterate.

Build a high-throughput GPU-based simulation pipeline for robotics foundation model training, implement actuators/sensors/controllers, bridge sim-to-real with policy training, and develop real-to-sim asset pipelines in collaboration with robotics and asset teams.

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.

Full-cycle technical recruiter focused on sourcing and closing elite Research Scientists, Software Engineers, and Robotics engineers. Partner with founders and technical leaders to design searches, build scalable sourcing and screening workflows (in Ashby), run a high-quality candidate experience, and automate recruiting processes to scale hiring.

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 profiling and performance ownership of a hybrid raster/ray-traced renderer. Profile, diagnose, and optimize GPU/CPU rendering across platforms, build custom instrumentation, evolve hybrid paths, set technical direction, mentor senior engineers, and collaborate with hardware vendors on low-level GPU issues.

Full-cycle recruiter responsible for sourcing and closing elite Research Scientists, Software Engineers, and Robotics engineers. Partner closely with founders and technical leaders, build scalable sourcing and screening workflows (Ashby/ATS), run a fast, candidate-friendly hiring process, and use data and automation to optimize pipeline health and conversions.

Lead development of a high-performance robotics simulation platform and compiler stack. Design compute infrastructure and dataflows for physics simulation and foundation-model training. Implement JIT/LLVM-based GPU codegen, optimize compile/runtime performance, add differentiable programming support, collaborate with simulation engineers, and contribute to open-source ML compiler projects.

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.

Design and implement embedded control stacks, motion planning, and trajectory optimization for dynamic robots. Build real-time state-estimation pipelines fusing heterogeneous sensors, formulate and solve optimal control problems (nonlinear MPC, convex/trajectory optimization), create modular sim-to-hardware software, and lead on-robot debugging, tuning, and validation of controllers.

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.

Develop and optimize a learning-based robotic manipulation control stack; design and maintain low-latency teleoperation systems; train and deploy policies using reinforcement and imitation learning; diagnose control-pipeline latency and sim-to-real gaps; co-optimize simulation and real-world robot behavior; collaborate on building general-purpose Physical AI.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines, kernels, and networking. Implement low-level CUDA/cuDNN/Triton kernels, tune hardware-software interactions, and develop monitoring and debugging tools for large-scale training runs to maximize utilization and reduce time-to-convergence.

Build and maintain scalable simulation infrastructure and environment frameworks for robotics model development, evaluation, and iteration. Create sim-ready digital twins, tune physics/rendering to reduce sim-to-real gap, and develop tooling for reliable, observable simulation workflows. Collaborate with simulation engine, rendering, asset, AI, and robotics teams.

Design, build, and operate large-scale distributed systems to improve reliability, scalability, and efficiency. Automate toil, perform capacity planning and performance analysis, prevent incidents, and provide cross-team technical leadership to balance system health with product velocity.

Own customer-facing go-to-market for robotics: identify design partners, drive pilots to production, manage complex technical sales, translate customer insights to product and engineering, define pricing and deployment playbooks, and build long-term partnerships in operational environments.

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.