7 days ago
Redmond, WA, USASenior
Base Salary
$160k - $220k/yr
Responsibilities
- Design, build, fine-tune, evaluate, and deploy AI/ML systems across Vision AI, multimodal AI, agentic AI, and Physical AI use cases.
- Develop and integrate models for video understanding, image perception, tracking, multimodal reasoning, autonomous workflows, and robotics-related tasks.
- Productionize models using NVIDIA AI tools and cloud AI platforms including NVIDIA NeMo, Riva, RAPIDS, Triton, Isaac stack, AWS Bedrock, and GCP Vertex AI.
- Build large-scale pipelines for structured and unstructured video, audio, sensor, and text data.
- Implement model inference, evaluation, monitoring, drift detection, governance, benchmarking, error analysis, and model improvement workflows.
- Support LLM, VLM, world model, agent framework, tool-using agent, and memory-enabled agentic system experimentation.
- Contribute to simulation, digital twins, robotics perception, dexterous manipulation, long-horizon task execution, autonomous driving, and edge-case evaluation systems.
- Collaborate with customers, researchers, data scientists, and engineering teams to identify datasets, define success metrics, and translate business needs into AI system designs.
- Build reusable internal frameworks, accelerators, and data products for multimodal and agentic AI deployments.
Requirements
- Master’s degree in Computer Science, Machine Learning, or equivalent practical experience.
- At least 5 years of experience building and deploying large-scale AI/ML systems in production.
- Strong Python programming skills and experience with PyTorch, TensorFlow, or JAX.
- Hands-on Vision AI experience with image or video models, object detection, tracking, segmentation, grounding, video analytics, 3D vision, or multimodal perception.
- Experience with Generative AI systems including LLMs, VLMs, multimodal pipelines, RAG, agents, or agent orchestration frameworks.
- Familiarity with agentic AI concepts such as tool use, planning, workflow orchestration, and memory; agentic memory or knowledge-graph-backed agent experience is preferred.
- Experience with NVIDIA AI ecosystem tools such as NeMo, RAPIDS, Riva, and Triton, with Isaac Sim, Omniverse, or related simulation exposure preferred.
- Experience building scalable GPU-based inference or training pipelines and familiarity with performance optimization, distributed systems, or high-performance networking.
- Ability to design experiments, evaluate hypotheses, and implement optimization workflows for real-world AI systems.
- Strong communication skills and ability to work directly with customers, researchers, and cross-functional engineering teams.
- Preferred experience includes robotics, autonomous driving, simulation, digital twins, embodied AI, sensor fusion, audio/video analytics, multimodal data pipelines, robotics data formats, model governance, observability, safety evaluation, and production model monitoring.
- Familiarity with Ray, Kubernetes, Docker, FastAPI, TensorRT, MLflow, Weights & Biases, or related MLOps and distributed AI tooling is preferred.
Benefits
- Work location options include Seattle, Palo Alto, or remote.
- Collaborate with engineering, data science, simulation, and robotics teams on frontier Vision AI, agentic AI, and Physical AI problems.
- Help connect research-grade models to real-world deployments and shape AI systems for real-world action.
Tech Stack
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