We are hiring for Lead Data Scientist at Santa Clara, CA Onsite
Title: Lead Data Scientist with Computer Vision
Focus area: GenAI, Agentic & Computer Vision Solutions
Applied Materials: AIML Team
Location: On-site (Santa Clara)
About the Role
We are looking for a Lead AI Engineer / Data Scientist: an AI-engineering-heavy practitioner who combines deep algorithmic and deep learning expertise with strong solutioning and customer-facing skills. This is a hybrid role: part senior AI engineer, part technical lead, and part trusted advisor to customers.
You will design and build production AI systems end to end across multiple domains: Generative and Agentic AI (including working with foundation models such as Claude), Computer Vision on unstructured data, forecasting, and optimization. Your work spans understanding the customer's problem and datasets, selecting the right algorithms, fine-tuning, and deploying models, architecting agentic workflows, and standing up the surrounding infrastructure. You will be the technical face of these solutions: guiding developers, resolving customer issues in real time, and translating ambiguous requirements into concrete, workable plans.
The ideal candidate has a strong track record of working with roughly 10+ AI/ML projects deployed to production and is as comfortable writing production deep learning code as they are sitting in front of a customer diagnosing an issue and proposing a path forward.
Key Responsibilities
Solution Design & Technical Leadership:
AI/ML Engineering & Modeling:
Required Qualifications:
Technical Frameworks & Toolkit:
Deep Learning frameworks: PyTorch, TensorFlow, Keras, JAX; PyTorch Lightning.
GenAI & fine-tuning frameworks: Hugging Face Transformers, PEFT (LoRA/QLoRA), TRL, Accelerate, DeepSpeed, bitsandbytes, Axolotl, Unsloth; vLLM / TGI / Ollama for serving; LangChain, LlamaIndex for RAG and orchestration.
Agentic AI frameworks & protocols: Claude Agent SDK, Anthropic / OpenAI SDKs, LangGraph, AutoGen, CrewAI, Semantic Kernel, and the Model Context Protocol (MCP); tool/function calling and multi-agent patterns.
Computer vision: OpenCV, Detectron2, Segment Anything (SAM); image/video pipelines.
Forecasting & optimization: stats models, Prophet, GluonTS, Darts, scikit-learn; optimization/solver tooling (e.g., OR-Tools, SciPy, PuLP, Gurobi/CVXPY).
MLOps & infra: experiment tracking (MLflow / Weights & Biases), Docker, Kubernetes, CI/CD for ML, model registries and monitoring.