AI Deployment Engineer - Startups
Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement. Prototype and iterate on prompts, agents, and workflow designs to better understand system behavior and unlock customer value. Synthesize and deliver valuable feedback to the Product and Research teams, turning real usage patterns into clear, reproducible evaluations, benchmarks, and technical artifacts that improve model and product quality and ensure customer-grounded learnings influence roadmap and model development. Build repeatable tools, patterns, and evaluation approaches that raise the quality bar across multiple use cases. Operate with strong judgment in ambiguous environments, balancing immediate technical problem-solving with longer-term system improvement. Build relationships within the startup ecosystem, serving as a technical partner to both individual customers and the broader community.
Software Engineer (Brazil)
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
Optical Engineer - Freelance AI Trainer
Design original optics problems that simulate real physics research workflows; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; base problems on real research challenges or practical applications from optics and physics practice; document problem statements clearly and provide verified correct answers.
Member of Technical Staff, Training (Paris, London)
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 including 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, Platform (Paris, London)
Design, build, and maintain foundational frameworks and tools to empower expert teams to experiment fast and turn ideas into production-ready systems. Collaborate with expert teams to validate use cases and build robust solutions, aiming for modular and reusable components. Identify and mitigate high-level code design flaws and development workflow inefficiencies that cause friction and hinder productivity across the organization. Advocate for good practices and maintain high code quality through code reviews, documentation, and training.
Member of Technical Staff, Simulation (Paris, London)
Develop a high-throughput, GPU-based simulation pipeline primarily for 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 committed to building general-purpose Physical AI.
Member of Technical Staff, Robot Learning (Paris, London)
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, Robotics (Paris, London)
Design, implement, and optimize the embedded control stack for general-purpose robots. Design motion planning and trajectory optimization algorithms for dynamic locomotion and manipulation. Build real-time state estimation pipelines for pose, contact, and force sensing by fusing heterogeneous sensor data under noise and uncertainty. Formulate and solve optimal control problems including nonlinear MPC, convex optimization, and trajectory optimization for high-performance and stable behavior. Build modular and robust software frameworks enabling rapid iteration between simulation and hardware. Lead debugging, tuning, and validation of controllers directly on physical robots.
Member of Technical Staff, Rendering (Paris, London)
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, Inference (Paris, London)
Build low-latency inference pipelines for on-device deployment to enable real-time next-token and diffusion-based control loops in robotics. Design and optimize distributed inference systems on GPU clusters to increase throughput with large-batch serving and efficient resource utilization. Implement efficient low-level code such as CUDA, Triton, and custom kernels, integrating it seamlessly into high-level frameworks. Optimize workloads for both throughput and latency by managing batching, scheduling, quantization, caching, memory management, and graph compilation. Develop monitoring and debugging tools to ensure reliability, determinism, and rapid diagnosis of regressions across both software stacks.
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