Staff Engineer, Applied ML

Circuit; Austin (TX); On-site; USD 180,000 - 270,000; Full time

Job summary

Circuit is seeking a Staff Engineer, Applied ML in Austin, TX to build ML capabilities powering our industrial product. You’ll collaborate with Forward Deployed Engineers, Product Engineers, and design customers to translate real-world challenges into scalable ML solutions.

You’ll own end-to-end delivery, balancing accuracy, latency, and cost while shaping product capabilities for multiple customers. You will work with LLMs, traditional ML, and a mix of models, ensuring robust evaluation,

Qualifications

• Strong software engineering fundamentals and ability to ship end-to-end production systems.
• Deep experience with data modeling across relational and graph databases.
• Hands-on experience building with LLMs, RAG systems, and agent harnesses.
• Experience working with customers to turn feedback into product features.
• Ability to generalize a solution to multiple customers.
• Proficiency in Python and Go.
• Experience with AI coding tools and prompt iteration.

Responsibilities

• Work directly with FDEs and design-customers end to end, to build solutions on top of Circuit that solve their challenges.
• Generalize and package our solutions and methods, and empower FDEs to efficiently deploy them across customers.
• Improve our underlying models, harnesses, eval methodologies and more to power our solutions and help solve customer challenges.
• Debug production systems including edge cases and failure modes in ML pipelines and retrieval systems.
• Build and maintain evaluation systems, including datasets, scoring approaches, and repeatable testing to detect regressions.

Skills

Python

Go

PyTorch

LLMs

RAG systems

AI deployment

Tools

KServe

vLLM

CPUs/GPUs

Job description

About Circuit

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 Staff Engineer, Applied ML at Circuit, you'll join Product Engineering and build the machine learning capabilities that power our product. You'll work closely with Forward Deployed Engineers (FDEs), Product Engineers, and directly with design customers to understand real-world challenges and turn them into robust, scalable solutions. A core part of your role is recognizing where a customer need points to a broader product capability, then building it to work across customers and use cases.

This is a hands-on engineering role focused on solving current product and customer problems. You'll use advances in ML where they help, exercise judgment about what is ready to ship, and own solutions end to end, with attention to accuracy, reliability, latency, and cost. Success means delivering capabilities that make Circuit's product more useful, reliable, and broadly applicable.

You'll draw primarily on LLMs, alongside traditional NNs and statistical models, choosing and combining approaches based on the problem. Your work will span understanding customer use cases and how users interact with the product, data and process modeling, understanding what exists today and how to build on it, evaluation, production implementation, and, where it pays off, model training and fine-tuning. Most importantly, you'll work end to end with our design customers, building solutions for them. Beyond individual features, you'll help build shared capabilities such as Circuit's evaluation framework, improving how we measure quality and catch regressions across the product. As priorities evolve, you'll apply your engineering judgment wherever it can make the greatest impact.

Product Engineering builds the core platform and works directly with customers and design partners. Our FDE teams own solutions architecture and technical demonstrations. You'll work alongside them on what gets built, and partner with Core ML engineers when a customer problem calls for deeper work on our document models or the agent loop. Your teammates range from full-stack engineers to applied ML researchers.

This position is based in Austin, TX, onsite 3 days/week.

What Is in Production Today

Our core product includes a long-running agent loop written in Go. Frontier model APIs drive the loop, while specialized tools handle retrieval, extraction, and agent execution. These tools let us solve industrial problems that general-purpose agent harnesses struggle with.

We use a combination of open-source and internally fine-tuned models, particularly vision-language models (VLMs), hosted on KServe. Our customer data includes technical documents, high-resolution blueprints, raster images, vector geometry, CAD, and structured data.

Key Responsibilities

• Work directly with FDEs and design-customers end to end, to build solutions on top of Circuit that solve their challenges

• Generalize and package our solutions and methods, and empower FDEs to efficiently deploy them across customers.

• Improve our underlying models, harnesses, eval methodologies and more to power our solutions and help solve customer challenges.

• Debug production systems including edge cases and failure modes in ML pipelines and retrieval systems.

• Build and maintain evaluation systems, including datasets, scoring approaches, and repeatable testing to detect regressions.

Examples of Work You Could Own

• Build a solution with a design customer. Work with an FDE and a design customer to turn a judgment-heavy workflow, from quoting a configured product to troubleshooting in the field, into an agent that gets it right.

• Model the customer's data. Turn a customer's spreadsheets, PDFs, and product rules into a data and process model the agent can reason over.

• Generalize it. Package a solution built for one customer so FDEs can deploy it for the next customer without rebuilding it.

• Evaluate on real data. Build an evaluation set from messy customer data, measure where the agent gets it wrong, and catch regressions as the product changes.

• Improve retrieval. Improve retrieval quality, relevance, or latency for a customer‑specific use case.

• Debug production. Trace an edge case in a live customer workflow to root cause with observability tools, and fix it in the product rather than with a one‑off patch.

Experience

We do not need to be an expert in every area. Strong software engineering fundamentals, good judgment with customers, and the ability to turn one customer's solution into a product capability matter more than familiarity with our exact stack. Beyond that, we look for:

• Retrieval systems, including embeddings and ranking

• Model training and fine‑tuning for production, across LLMs, deep learning, or traditional ML

• Evaluation design and dataset curation from messy, real‑world customer data

• Debugging production ML systems with AI observability tools

We do not need to be an expert in every area. Strong software engineering fundamentals, good judgment with customers, and the ability to turn one customer's solution into a product capability matter more than familiarity with our exact stack. Beyond that, we look for:

• Strong software engineering background, with a track record of owning and shipping production systems end to end.

• Deep experience with data structures and data modeling across relational and graph databases, with strong, well-reasoned opinions on schema design, normalization, and indexing strategy.

• Hands‑on experience building with LLMs, RAG systems, and agent harnesses, with a solid grounding in how these systems work, fail, and need to be evaluated.

• Experience working directly with customers or field teams, turning what you learn into what gets built.

• A track record of taking a solution built for one customer and generalizing it so others can deploy it.

• Built and maintained features across a multi‑service architecture, with strong proficiency in languages like Python and Go.

• Daily, fluent use of AI coding tools. You write effective prompts, iterate when output is wrong, and reliably tell working code from code that only looks right.

• Bonus: Vision and multimodal models, especially on technical documents (PDFs, CAD) with text, vector geometry, and raster content.

• Bonus: Construction, civil engineering, or other industrial domains.

• Bonus: Open‑source models (Qwen, DeepSeek, or similar), and serving them with vLLM, KServe, or similar.

• Bonus: Hands‑on PyTorch (or similar) for training and fine‑tuning models.

Human Skills

• Ownership mindset with a focus on delivering working systems in production.

• Strong product judgment, with an understanding of how system behavior impacts user experience and trust.

• Rigorous reviewer of agent instructions, skills, and documentation, ensuring they're clear, unambiguous, and reliable for agents to act on.

• Curiosity about how systems behave in practice and how customers use them.

• Bias toward shipping. You take ambiguous problems and drive them to working solutions without needing the path fully defined for you.

• Judgment about what is ready to ship, and the willingness to push back to keep the work grounded in customer outcomes.

• Clear communicator. You explain ML tradeoffs to FDEs and customers in plain terms, and share progress early.

• Critical evaluation of AI output. You spot hallucinated APIs, incorrect logic, and subtle bugs