Genesis AI

• Lead the evolution of our high-performance robotics simulation platform • Design and implement the compute infrastructure and data flow mechanisms to optimize performance for physics simulation and foundation model training • Lead development of our compiler stack, focusing on JIT compilation, LLVM IR, and GPU codegen to minimize compile time and maximize runtime performance • Collaborate with the team to improve the compiler's support for differentiable programming, crucial for training neural networks within simulations • Stay current on state-of-the-art ML compilers—such as those in torch, Triton, and JAX—and decide which techniques and approaches are best suited for our application • Work closely with simulation and robotics engineers to align compiler enhancements with application needs • Contribute to relevant open-source projects and participate actively in the broader compiler and systems community

Role Description - Develop a high-throughput rendering pipeline for training robotics foundation models - Design protocols and interfaces between the rendering pipeline, physics engine, and 3D generative models - Build an efficient platform for large-scale robotics training and evaluation - Build a closed-loop system to minimize the sim-to-real gap in rendering for robotics policy training - Collaborate with a team of driven individuals committed to building general-purpose Physical AI Qualifications - Passion for your craft and demonstrated excellence in rendering and graphics engineering - Exceptional ownership and initiative—finding and solving problems independently - Extensive experience writing low-level code for industry-standard real-time rendering pipelines like the Unreal Engine (8+ years) - A deep and holistic understanding of rendering pipelines - Production-level expertise in C++ and CUDA Requirements - Bonus: Experience in physics simulation

Role Description - Develop a high-throughput, GPU-based simulation pipeline (primarily rigid body simulation for robots) to train robotics foundation models - Implement essential robotics features, including actuators, sensors, and controllers, in collaboration with the robotics team - Work with the policy training team to bridge the sim-to-real gap - Develop a real-to-sim pipeline in collaboration with the asset generation team - Collaborate with a team of driven individuals committed to building general-purpose Physical AI Qualifications - Passion for your craft and demonstrated excellence in physics simulation engineering - Exceptional ownership and initiative—finding and solving problems independently - Extensive experience in physics simulation, with a preference for expertise in rigid body simulation, FEM, CFD - Insights and requests from a robotic researcher/engineer’s perspective for improving user experience - Proficiency in CUDA and GPU programming Requirements - Bonus: Experience in robotics control

Role Description - Build low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics. - Design and optimize distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization. - Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate it seamlessly into high-level frameworks. - Optimize workloads for both throughput (batching, scheduling, quantization) and latency (caching, memory management, graph compilation). - Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks. Qualifications - Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years). - Production-grade expertise in Python, with strong background in systems languages (C++/Rust/Go). - Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling. - Proven track record scaling inference workloads in both throughput-oriented cluster environments and latency-critical on-device deployments. - System-level mindset with a history of tuning hardware–software interactions for maximum efficiency, throughput, and responsiveness.

Role Description - Develop LM/VLM-powered agents that autonomously interface with code, GUI, web tools, and design software to drive generative simulation. - Build agentic systems that synthesize high-quality, diverse physical data by orchestrating simulation, generative models, and interactive tools. - Build data pipelines to curate, generate, and preprocess datasets used for training and evaluating language- and vision-language-based agents. - Collaborate with simulation and infrastructure teams to co-develop APIs, GUIs, and services that enable seamless interaction with physics engines and simulation environments. - Drive innovation by staying at the forefront of LLM agents, autonomous tool use, and generative simulation — and translating cutting-edge research into robust, production-ready systems. - Contribute to the creation of a generative simulation stack that empowers downstream applications in robotics, embodied AI, and physical reasoning. Qualifications - A bold and imaginative vision for a next-generation paradigm of physical data synthesis— combining simulation, generative models, and autonomous agents. - Deep curiosity and strong technical ownership, with a track record of driving complex, open-ended projects from concept to implementation. - Experience with (multimodal) large language models, generative AI tools, agentic software design, GUI automation, program synthesis. - Passion for inventing creative solutions at the edge of AI, simulation, and physical reasoning, with an eagerness to build systems that generate high-fidelity, useful data for embodied intelligence. Requirements - Familiarity with interactive design tools (e.g., CAD software, game engines) or simulation environments (e.g., physics engines, robotics simulators).

Role Description - Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training stack, from data pipelines to GPU kernels. - Design, build, and optimize distributed training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization. - Implement efficient low-level code (CUDA, cuDNN, Triton, custom kernels) and integrate it seamlessly into high-level training frameworks. - Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking. - Develop monitoring and debugging tools for large-scale runs, enabling rapid diagnosis of performance regressions and failures. Qualifications - Deep experience in distributed systems, ML infrastructure, or high-performance computing (8+ years). - Production-grade expertise in Python. - Low-level performance mastery: CUDA/cuDNN/Triton, CPU–GPU interactions, data movement, and kernel optimization. - Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism. - System-level mindset with a track record of tuning hardware–software interactions for maximum utilization.

Role Description - Design, build, and maintain large-scale data pipelines (batch and streaming) for robotics foundation model training and evaluation at petabyte scale. - Own core data infrastructure: data model, storage systems, ingestion pipelines, transformation frameworks, and orchestration layers. - Standardize data models and unify processing pipelines across real-world teleoperation and synthetic simulation datasets. - Collaborate with a team of driven individuals committed to building general-purpose Physical AI. Qualifications - Excellent software engineering skills (Python, Go, or similar). - Extensive experience designing, building, and maintaining large-scale data pipelines (8+ years). - Deep understanding of distributed systems (Spark, Kafka, or similar). - Extensive experience with data storage technologies (data lakes, warehouses, object stores like S3). - Experience running and maintaining production-grade infrastructure (Kubernetes, Terraform). Requirements - Bonus: Experience supporting AI systems, in particular embodied AI like self-driving.

• Design, build, and maintain foundational frameworks and tools to empower expert teams to experiment fast and turn ideas into full-fledged and production-ready systems. • Collaborate with expert teams to validate uses cases and build robust solutions, bringing cross-team perspective and aiming for modular and reusable components. • Identify and mitigate high-level code design flaws and development workflow inefficiencies that cause friction, hinder productivity, and create silos across the organization. • Advocate for good practices and maintain high code quality through code reviews, documentation, and training.

• Design, build, and maintain foundational frameworks and tools to empower expert teams to experiment fast and turn ideas into full-fledged and production-ready systems. • Collaborate with expert teams to validate uses cases and build robust solutions, bringing cross-team perspective and aiming for modular and reusable components. • Identify and mitigate high-level code design flaws and development workflow inefficiencies that cause friction, hinder productivity, and create silos across the organization. • Advocate for good practices and maintain high code quality through code reviews, documentation, and training.