Staff Machine Learning Engineer

Position :: Staff Machine Learning Engineer

Location :: San Jose CA USA- Onsite

Long term contract

Job 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: You will architect the entire data lifecycle including our CI/CD pipelines data-labeling loops and on-device monitoring.
• Agentic & Edge AI: You will lead the design of autonomous agents that run on our edge devices using domain knowledge in log analysis and computer vision.
• Systems & Hardware: You will be the hardware-aware expert bridging our ML software with our silicon team to ensure our models are hyper-optimized for our custom NPU.
• 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 elses.

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.

Warm Regards