2 months 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
Categories
About Centific
Centific builds AI data and engineering platforms and services for enterprises, including curated multimodal datasets, industry‑tuned LLMs, and RAG pipelines backed by vector databases. It monetizes through software, data operations, and consulting to design, deploy, and scale GenAI systems, along with QA automation and digital engineering work. Privately held and headquartered in Redmond, Washington, the company raised a $60M Series A in 2025 to expand its AI data foundry and platform capabilities.