Staff ML Product Engineer

Circuit•Austin, TX

About The Position

Circuit is building the worldâ€™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. 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 closely with ML engineers who focus on models and approaches. Your responsibility is ensuring those components are integrated, tested, and behave correctly within the full system. 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.

Requirements

Strong software engineering background, with experience building and owning production systems end to end.
Strong proficiency in Python, with a track record of building well-structured and maintainable systems.
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.
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.
Familiarity with Go and ability to work in a multi-language backend environment.
Experience working with customers or customer-facing systems, incorporating feedback into what gets built.
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.
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.

Nice To Haves

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.

Responsibilities

Own delivery of ML-powered features from concept through production. You are responsible for the feature working in practice.
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.
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.