Hiring: AI Engineer Company: Noösphere Location: Greater Seattle Area Job Posted Time: 2026-09-16 07:37:44 Target Skills & Keywords : AWS, C++, CI/CD, Data Pipeline, Embedded Systems, Firmware, GCP, GDPR, Kind, Kubernetes, MLOps, ONNX, PyTorch, Python, Rust, TensorRT, Triton, vLLM About the job Experience: •5+ years building and shipping ML systems in production, with a record of models you trained or deployed reaching real users. Required Skills: •Human-centered physical intelligence starts with •Models that learn from how people actually do things in the real world •. Our systems run in homes and workplaces with people doing real tasks, and every deployment returns hours of continuous, time-synchronized multimodal data. Nothing comparable exists publicly, and the models and benchmarks for this kind of data have yet to be built. •Strategically drive path from a trained checkpoint to a deployed system — export, quantization, distillation, and inference optimization across edge and cloud targets under real-time constraints. •Build and operate the serving and streaming infrastructure that runs models over continuous multimodal input, in real time and offline over recorded data. •Build the telemetry that measures how models behave in deployment and feeds failures and edge cases back into the data engine. •Set the engineering bar for the ML stack: reproducible training runs, versioned datasets and models, tested pipelines, and CI for model changes. •Build the tools and abstractions that let the team experiment quickly without hitting infrastructure walls. Qualifications: •Embedded or edge deployment on constrained hardware, including firmware-adjacent debugging •Published or open-source work grounded in real-world deployment •Cross-embodiment learning, world models, or sim-to-real transfer •Learning from demonstration or policy learning over multimodal sensor streams — vision-language-action models, diffusion policies, or behavior cloning from teleoperation data •Simulation environments — Isaac, MuJoCo, or similar — for training, evaluation, or data generation •Robot data infrastructure and open ecosystems — ingesting and time-aligning multi-camera, proprioceptive, and joint-state streams (ROS bags, MCAP, LeRobot datasets, Open X-Embodiment) •Policy deployment and evaluation on real robots — real-time inference loops, action chunking, safety monitors, task suites, and rollout infrastructure •You'll be working with a tightly knit team that has pioneered frontier AI research, shipped tens of millions of devices, scaled infrastructure used by millions every day, and defined how people and machines interact in the physical world. They've done it at Meta, Amazon, Microsoft, and Valve. •Early role at a funded startup founded by the team that built one of the most advanced physical AI data engines in the industry. •Strategically drive path from research to production for models no one has trained before, on real-world multimodal data that no lab has. Compensation: •Competitive salary plus meaningful early-stage equity and benefits Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!