Staff Machine Learning Engineer
San Jose, CA, USA
Department: ML
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:
1. MLOps & Data: You will architect the entire data lifecycle, including our CI/CD
pipelines, data-labeling loops, and on-device monitoring.
1. 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.
1. 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
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 development of 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.
Preferred Qualifications (The "Plus" Factors)
● Direct experience with ML compilers such as Apache TVM or MLIR.
● Hands-on experience with Kubernetes (K8s) for MLOps (e.g., Kubeflow, Argo).
● Familiarity with GPU scheduling and virtualization platforms like Run:AI.
● Deep experience with embedded system development (C++, Rust, Yocto).
● Familiarity with the RISC-V instruction set architecture (ISA).
● Proficiency with cloud platforms (AWS, Google Cloud Platform, Azure).
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