Staff Engineer, Core ML

Key Responsibilities

- Develop algorithms that extract meaning from complex technical documents containing text, vector geometry, CAD, and raster content, spanning vision models, geometric algorithms, and language understanding.

- Build those document understanding capabilities into the tools that power Circuit's long-running agentic systems.

- Improve the reliability, determinism, and effectiveness of agentic systems, including agent planning, tool use, and failure recovery.

- Build and extend training and evaluation pipelines for our core models and agents, so every change is measured against real customer data before it ships.

- Analyze how our algorithms perform in production, find where and why they fail, and turn that into the next improvement.

- Find gaps and bias in our datasets, and close them through curation, labeling, or synthetic data.

- Track new research and techniques, and make the promising ones work in production.

- Mentor and elevate other engineers on the team.

Examples of Work You Could Own

- Fix a failure class. Trace why extraction breaks on a class of drawings, such as dense electrical schematics, fix it with better data, a better model, or a better algorithm, and show the lift in the evals.

- Fine-tune a specialized model. Fine-tune a LoRA on an in-house model to extract a specialized artifact from a high-resolution blueprint page.

- Bring a new algorithm to production. Take an approach from a recent paper, such as a new layout or table-structure model, prove it beats what we run today on our data, and make it fast and reliable enough to ship.

- Improve retrieval. Improve retrieval quality, relevance, or latency across document types and customers.

- Make agents more deterministic. Find why the same task produces different results from run to run, and change the tools or the loop so it stops.

Experience

We want someone who has shipped ML-powered software to production, can adapt proven approaches and evaluate alternatives from other production ML systems, and measures whether a change works for customers. Ideally, you have deep expertise and strong technical opinions in at least one of these areas:

- Agent harness design and execution

- Document extraction and structured data understanding

- High-resolution computer vision

- VLM fine-tuning and inference

- Retrieval systems, including embeddings and reranking

- Agent evaluation and optimization

You do not need to be an expert in every area. Strong software engineering fundamentals and the ability to lead technical decisions matter more than familiarity with our exact stack. Beyond that, we look for:

- Hands-on AI/ML engineering experience, with a track record of shipping models in production.

- Experience training and fine-tuning models for production, including the datasets and pipelines behind them.

- Experience building and operating ML evaluation pipelines, including datasets, metrics, and regression testing for models and agents in production.

- Experience profiling data distributions, identifying gaps and bias, and curating high-quality datasets for training and evaluation.

- Experience with vision and multimodal models, especially on technical documents (PDFs, CAD) with text, vector geometry, and raster content.

- Strong knowledge of data structures, algorithms, and software engineering principles.

- Proven expertise with LLMs, deep learning systems, and applied ML.

- Experience debugging production ML systems using AI observability tools (e.g. Langfuse, Opik, etc.), tracing failures to root cause.

- Proficiency in Python and relevant ML frameworks (PyTorch, vLLM, etc.).

- 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: Computational geometry, or experience with vector drawing formats such as CAD and vector PDFs.

- Bonus: Go, the language our agent loop is written in.

- Bonus: Familiarity with construction, civil engineering, or related industries where interpreting complex technical drawings and specifications is central to the work.

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 engineers, FDEs, and product stakeholders in plain terms, and share progress early.

- Critical evaluation of AI output. You spot hallucinated APIs, incorrect logic, and subtle bugs before they reach production.

- Systems thinking, including attention to correctness, determinism, and failure modes in long-running agents.

- Rigor about measurement. You measure before you claim an improvement, and you can tell a real gain from noise in an eval.

- Raises the bar for the team through design reviews, code reviews, and mentoring.

- Low ego, with a focus on team outcomes.

What We Offer

- High-caliber team of seasoned manufacturing and AI leaders

- Help create a category-defining platform transforming how manufacturers sell, support, and innovate

- Competitive compensation, meaningful equity, and employer-paid benefits

- Flexible PTO and access to the latest AI tools

- A values-driven culture that rewards ownership, curiosity and velocity