Genesis AI

Software Engineer

• 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

Member of Technical Staff, Inference

• Build Vision-Language-Action models for robotics control end-to-end: data curation, careful evaluation, model architecture, training/inference stacks, rigorous experiments
• Collaborate with simulation and real-world robotics teams to curate high-quality, diverse, and large-scale datasets
• Curate the world’s best Internet-scale datasets for embodied perception and first-person robot video generation
• Design new generative simulation techniques to expand simulation data scale and diversity, training and evaluating generative models of 3D objects and environments, and language/code models to generate tasks and reward functions
• Collaborate with a team of driven individuals committed to building general-purpose Physical AI

Member of Technical Staff, Foundations

• 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

Member of Technical Staff, Simulation

• 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

Member of Technical Staff, Data Agent

• 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

Member of Technical Staff, ML Compiler and Systems

• Develop and optimize a learning-based robotic manipulation control stack
• Design and maintain a teleoperation system with smooth, precise motion and low latency
• Train robotic policies for manipulation and locomotion with reinforcement learning and imitation learning
• Deploy robotic policies and diagnose latency or bottlenecks in the control pipeline
• Analyze and minimize the sim-to-real gap by co-optimizing simulation and real-world robot behavior
• Collaborate with a team of driven individuals committed to building general-purpose Physical AI

Member of Technical Staff, Robot Learning

• 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

Member of Technical Staff, Data

• 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

Member of Technical Staff, Rendering

• Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training stack 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

Member of Technical Staff, 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.