ML Efficiency Engineering Lead — Systems & Scale

Polluxa, Inc.; Mountain View (CA); On-site; USD 210,000 - 270,000; Full time

Job summary

Polluxa, Inc. is seeking an experienced ML Systems Architect to lead technical pathfinding for the ML Efficiency Hub, shaping design and implementation for production-grade ML pipelines.

You will prototype in C++ and Python, address latency and scalability constraints, and collaborate with cross-functional teams to deliver robust, scalable solutions in a fast-paced startup environment.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years testing, launching software products, and 3 years in design/architecture.
• 5 years in ML areas: speech/audio, reinforcement learning, ML infrastructure, or related field.
• 5 years in ML design and ML infrastructure (deployment/evaluation/data processing/debugging).
• Experience integrating generative AI tools or LLM interfaces into workflows.

Responsibilities

• Lead technical pathfinding and system design for the ML Efficiency Hub, guiding Engineers and Sprints.
• Design and write production C++ and Python code for model distillation, decoding, batching, and distributed serving.
• Translate vague executive mandates into feasible plans under latency/FLOPs/tokenomics constraints to deliver TVPs.
• Refactor legacy pipelines, weigh architectural trade-offs, and define Graceful Exit Packages for autonomous operation.

Skills

Software development

Testing & launch

System design & architecture

ML & AI familiarity

Generative AI integration

Education

Bachelor's degree

Master's degree

PhD

Tools

C++

Python

XManager

BrainServer

SavedModel

Pathways

Job description

Polluxa, Inc. is seeking an experienced ML Systems Architect to lead technical pathfinding for the ML Efficiency Hub, shaping design and implementation for production-grade ML pipelines.

You will prototype in C++ and Python, address latency and scalability constraints, and collaborate with cross-functional teams to deliver robust, scalable solutions in a fast-paced startup environment.