About Circuit

Circuit is building the world’s first manufacturing-focused AI knowledge and workflow platform. Our mission is to help industrial and manufacturing enterprises transform how they sell, support, and collaborate with their dealer, distributor, and customer networks by turning complex product data and documentation into actionable, intelligent workflows, from partner enablement to CPQ to technical support.

The Role

As a Staff Applied ML Engineer, you are responsible for turning ML capabilities into production-ready product features.

This is a hands-on IC role with high ownership. You are accountable for ML-driven features working end to end. That includes how models, retrieval systems, and agent workflows come together, how they behave in real usage, and whether they meet customer expectations.

You will work side by side with our ML experts, taking ideas and novel approaches and driving them into reliable, production-ready systems that are integrated, tested, and behaving correctly within the full product.

You should be comfortable moving quickly to prototype and validate ideas, while also bringing the engineering discipline needed to build reliable, testable, and maintainable systems. A core part of the role is improving the quality of the ML codebase over time without slowing down iteration.

AI coding tools are core to how you work every day. You use them fluently to explore, build, and test, and you have the judgment to evaluate what they produce. We ship fast and we ship with quality.

Key Responsibilities

Own delivery of ML-powered features from concept through production. You are responsible for the feature working in practice.
Work side by side with our ML experts to drive ideas into reliable, production-ready systems.
Engage directly with customers and internal stakeholders to understand real-world usage and translate those insights into product and engineering decisions.
Use AI coding tools (Cursor, Claude Code, or similar) as a primary part of your daily workflow to read, write, and test code, while critically evaluating output and catching subtle errors before they ship.
Ensure models, retrieval systems, and agent workflows function correctly together across the full system.
Lead implementation of ML-driven features, coordinating with ML engineers and the rest of the team to get features shipped.
Build and maintain evaluation systems, including datasets, scoring approaches, and repeatable testing to detect regressions.
Design and iterate on prompts and agent instructions to ensure correct and predictable system behavior.
Establish and improve observability, debugging, and testing practices for ML systems.
Improve the structure, reliability, and maintainability of the ML codebase while preserving development speed.
Work primarily in Python, and contribute in Go and other languages where needed.
Modify and work with pipelines, retrieval systems, and model behavior when required.
Advocate for the right technical approach and push back when needed to keep the work grounded in customer outcomes.
Orchestrate workflows across APIs, external systems, and multiple data sources.
Balance rapid experimentation with longer-term system quality.
Work with customers and internal stakeholders to ensure solutions align with real-world usage.

Experience

• Strong software engineering background, with experience building and owning production systems end to end.
• Strong proficiency in Python and Go, with a track record of building well-structured and maintainable systems.
• Daily, fluent use of AI coding tools (Cursor, Claude Code, or similar) as a core part of your engineering workflow. You write effective prompts, iterate when output is wrong, and reliably distinguish working code from code that only looks right.
• Experience delivering complex features in production environments, ideally involving ML or AI systems.
• Demonstrated ability to take ownership of ambiguous problems and drive them to working solutions.
• Hands-on experience building with LLMs, RAG systems, and agent-based workflows, with a solid conceptual grounding in how these systems work, fail, and need to be evaluated.
• Experience working with LLMs, RAG systems, or agent-based workflows.
• 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 systems, including handling edge cases and failure modes.
• Experience with observability and debugging in ML or backend systems.
• Experience working with pipelines, retrieval systems, or model behavior such as ranking, fine-tuning, or prompt tuning.
• Comfortable operating in fast-moving environments with high ownership.
• Experience working with customers or customer-facing systems, incorporating feedback into what gets built.
• Familiarity with frontend or full-stack development.
• Experience with MLOps systems, data pipelines, or production ML infrastructure.
• Familiarity with open source models such as LLaMA, Qwen, DeepSeek, Kimi, or similar.

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.
Critical evaluation of AI output. You spot hallucinated APIs, incorrect logic, and subtle bugs in generated code before they reach production.
Bias toward action, with a focus on learning through building and iteration.
Ability to operate effectively in ambiguous environments.
Systems thinking, including attention to correctness and failure modes.
Curiosity about how systems behave in practice and how customers use them.
Low ego, with a focus on team outcomes.

What We Offer

Early-Stage Ownership: Join at the ground floor of a company with real traction and momentum.
Empowered Culture: We value autonomy, candor, and craft. You'll be trusted to lead.
Cutting-Edge Tech: Work with the latest in AI, backend systems, and intelligent infrastructure.
Meaningful Impact: Shape a platform that transforms how organizations activate knowledge.
Holistic Benefits: Competitive comp, equity, 100% paid healthcare, 401K, flexible PTO, and a team that truly cares.

Equal-Opportunity Employer

Who Should Apply

If you're an engineer who understands operational ML, the practical side of making these systems work in production rather than the deep algorithmic internals, and you're energized by the product side of what you're building, we'd love to hear from you. You work AI-native every day, ship real systems, and have the judgment to know when AI-generated code is ready for production. The problems here are genuinely hard, the customer impact is real, and we move fast.