Principal Engineer, ML Systems

Circuit•Austin, TX

About The Position

Circuit is the AI execution layer for the physical economy. Industrial and manufacturing enterprises run on expertise: the hard-won knowledge of how complex products are engineered, configured, sold, and supported. That expertise is scarce, slow to transfer, and walking out the door as a generation of experts retires. It lives in PDFs, spreadsheets, and the heads of a few people, and the hardest technical decisions wait on whoever happens to know. Circuit puts expert judgment in everyone's hands. We capture how the best people make critical technical decisions and turn it into agents that do the judgment-heavy work, so anyone, regardless of skill level, can make the right call, from quote to install and support, with the accuracy these environments demand. Circuit is built by operators. Our leadership team has built and scaled real industrial companies, and we are already live in production with category-leading manufacturers. We are not bolting AI onto an old playbook. We are building the layer that runs the last mile of how the physical economy executes. The Role As a Principal Technical ML Engineer, you are a strong software engineer first, with deep hands-on experience in ML systems and model serving infrastructure. This is not a research role and it is not an architecture role. It is a hands-on Principal IC role with high ownership where all your technical judgment shows up in shipped systems. You will work closely with other AI/ML engineers to understand what they are building and drive it through to shipped product. The gap between promising research and working software is where you operate. You bring the engineering discipline to close that gap reliably and repeatedly.

Requirements

Strong software engineering background with a track record of owning and shipping production systems end to end.
Genuine MLOps depth. You have run model serving and/or evaluation infrastructure in production, understand systems like KServe, and have real opinions about the serving stack. You are not someone whose experience stops at configuring managed cloud ML services. You understand the serving stack deeply enough to make tradeoffs, debug failures, and improve it.
Enough ML knowledge to be a real partner to the AI/ML team. You can follow what they are building, ask the right questions, and make sound engineering decisions about productionizing it. You are not training models.
Experience with LLMs, RAG systems, or agent-based workflows at a level deeper than stringing together API calls.
Strong proficiency in Python. Comfortable in Go or willing to get there quickly.
Experience integrating multiple systems, APIs, and data sources into cohesive product functionality.
Experience designing or working with evaluation systems for ML quality.
Experience debugging production ML systems including handling edge cases and failure modes.
Daily, fluent use of AI coding tools as a core part of your engineering workflow.
Ownership mindset with a focus on delivering working systems in production.
Bias toward shipping. You take ambiguous problems and drive them to working solutions without needing the path fully defined for you.
Engineering judgment about when research is ready to productionize and what it takes to get it there.
Critical evaluation of AI-generated output. You spot incorrect logic and subtle bugs before they reach production.
Systems thinking, including attention to correctness and failure modes.
Ability to operate effectively in fast-moving environments with high ownership.
Low ego, with a focus on team outcomes.

Responsibilities

Own the engineering work that moves ML capabilities from research into production. You write the code, you ship the feature, you are accountable for it working.
Work across the AI/ML team to understand their work and make sound engineering judgments about what is ready to ship and how to get it there.
Build and maintain model serving and evaluation infrastructure. You understand the stack deeply, including how inference works, why serving choices matter, and what the tradeoffs are.
Lead implementation of ML-driven features, coordinating with the research team and the rest of engineering to get things shipped.
Debug production systems including edge cases and failure modes in ML pipelines and retrieval systems.
Establish and improve observability, debugging, and testing practices for ML systems.
Build and maintain evaluation systems, including datasets, scoring approaches, and repeatable testing to detect regressions.
Improve the reliability, testability, and maintainability of the ML codebase without slowing down iteration.
Use AI coding tools fluently as a core part of your daily workflow, with the judgment to evaluate what they produce and catch errors before they ship.