Robot Science Ops

About Generalist At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done. We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world. The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness). We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. About the Role: This is a deeply cross-functional, execution-oriented ops role at the center of our model research and robotics stack. As a member of the Robot Science Operations staff, you will help turn robots, data, models, and evaluations into a tight, high-velocity feedback loop for model science. Foundation model intelligence is only as good as the evaluations we can hill climb on, and this role is responsible for making those evaluations real, reliable, and repeatable. You will execute experiments directed by our research teams on a variety of robot platforms. You will help design tasks, coordinate robot data collection, kick off training jobs, run evaluations, analyze results, and ensure robots are physically ready for rollouts. You may build physical benchmarks, lightly modify hardware setups, test third-party tooling, and write documentation that enables others to replicate and scale your work. You’ll be responsible for: Executing a range of experiments on our robot platforms Collaborate closely with the research teams on results and be required to synthesize and interpret your findings Designing new robotic tasks and benchmarks to evaluate model capabilities Procuring materials and building lightweight physical benchmarks Ensuring robots are properly configured, calibrated, and ready for rollouts and evaluations Running structured evaluations and measuring real-world success rates Analyzing results and closing feedback loops with ML researchers Beta testing internal and third-party tools for teaching robots new skills Writing clear documentation and playbooks so others can reproduce workflows Identifying operational bottlenecks and improving system throughput end-to-end You might thrive in this role if you: Be continuously diligent in the face of seemingly repetitive, but subtly changing task evaluations. Have hands-on experience with robot data collection, evaluation, or deployment Have conceptual understanding of the full modern ML training, fine-tuning, and inference life cycles Are comfortable running experiments and tracking real-world metrics across multiple model variants Enjoy operating across software, hardware, and physical systems Have some exposure to basic EE/ME tasks (wiring, mounting sensors, assembling fixtures, debugging hardware) Are highly organized and can coordinate multiple moving parts simultaneously Write clear, structured documentation Prefer execution and iteration speed over theoretical purity Like being the person who “just makes it work” What This Role Is Not You will be a part of the ML team, and working very closely with ML, brainstorming ideas, and may prototype as well. However: This is not a pure ML research role focused on designing new model architectures or advancing core learning algorithms. This is not a large-scale infrastructure engineering role building distributed systems, databases, or UI platforms. This is not a deep robotics controls or firmware engineering role. Instead, this role sits at the intersection of ML, robotics, and operations. You are ensuring our systems run end-to-end in the real world, and improving them through tight execution loops. If you are most excited by hands-on iteration, cross-functional execution, and accelerating the entire system this role may be a strong fit. #J-18808-Ljbffr Generalist