Staff Machine Learning Engineer
SA Technologies Inc
- San Jose, Arizona, United States
- San Jose, Arizona, United States
Über
Location San Jose, CA
Employment Type Full Time role
Client 100% Onsite role
Role Description This is a "full‑stack" ML systems role for a senior individual contributor and technical architect. You will be responsible for designing the complete ML ecosystem for our edge devices, from the cloud‑native MLOps platform down to the bare‑metal model optimization.
This unique role blends three key domains: MLOps & Data; Agentic & Edge AI; Systems & Hardware.
You are the engineer who will not only build our ML platform but also design the intelligent agents it deploys and ensure they run faster and more securely than anyone else's.
Key Responsibilities Architecture & Leadership
Act as a senior individual contributor, leading by example with hands‑on coding, design, and analysis across the entire ML stack.
Define the end‑to‑end architecture for our MLOps, agentic AI, and model optimization strategy.
MLOps & Data Platform
Design and implement our data processing and versioning pipelines, ensuring data integrity and traceability.
Build the infrastructure for our Human‑in‑the‑Loop (HITL) and AI‑in‑the‑Loop (Active Learning) data labeling systems to continuously improve our datasets.
Develop a comprehensive lightweight on‑device monitoring system to track not just operational metrics but also inference quality and concept drift.
Agentic & Edge Development
Design and develop autonomous agents that operate on our resource‑constrained edge devices.
Integrate deep domain knowledge, including real‑time log analysis, computer vision, and interaction with open‑source system tools.
Security & Optimization
Define and implement the complete security and verification framework for our edge models. This includes MCP/A2A‑like secure protocols, MCP authentication, entity verification (e.g., model signing), and model injection prevention.
Serve as the primary technical bridge to our silicon teams. Collaborate with RTL designers to influence future NPU and FPGA architecture from an ML software perspective.
Lead R&D on model optimization for our specific AI inference engine, applying both graph‑level (e.g., operator fusion) and OP‑level (e.g., custom ops) techniques.
Qualifications
8–10+ years of hands‑on experience in machine learning, with a proven track record as a senior or staff‑level individual contributor.
Ph.D. or M.S. in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).
Expert‑level programming in Python and deep experience with ML frameworks (e.g., PyTorch, TensorFlow).
Deep theoretical understanding of modern ML algorithms (e.g., Transformers).
A strong foundational understanding of computer architecture, digital logic, and the role of RTL (Verilog/VHDL) in the hardware design lifecycle.
Proven experience architecting and building end‑to‑end MLOps lifecycles, from data ingestion to production monitoring and labeling loops.
Proven experience developing agentic systems or applications using LLMs.
Demonstrable domain knowledge in log analysis and/or computer vision.
Experience with on‑device model security (verification, anti‑injection) and secure communication protocols.
Hands‑on experience optimizing models for hardware (NPUs, GPUs) at graph and operator levels.
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Sprachkenntnisse
- English
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