Staff Machine Learning Engineer

Job Description

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. 
  

Embark on a rewarding journey together with us! At SA Technologies Inc., your journey begins with a friendly technical assessment, providing a stage to highlight your unique skills.