Applied AI​/ML Engineer

About the job

Applied AI / ML Engineer

Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We stand out as an agency deeply embedded in our clients’ recruitment operations. We partner directly with AI‑first startups, established tech companies, and enterprise innovation teams, and we collaborate directly with founders, CTOs, and Heads of AI to drive the next wave of applied intelligence.

A San Francisco‑based startup building a GenAI‑native platform that automates the challenging process of turning dense tax documents into structured, usable data in minutes. The system processes everything from K‑1s and K‑3s to intricate footnotes with near‑human precision, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. It serves sophisticated private‑wealth and asset‑management players and has recently been acquired by a global technology leader, offering the blend of startup agility with enterprise stability.

Union Square, San Francisco – Full‑time, 5 days a week, on‑site.

Above market base + bonus + equity.

Sponsorship available for candidates with demonstrated brilliance and expertise.

We’re seeking an Applied AI / ML Engineer with 5+ years of experience building and scaling commercial machine learning systems in meaningful ownership roles, especially with document understanding and extraction. The ideal candidate is an exceptional builder who thrives at the intersection of real‑world features and AI/ML engineering and who not only understands how models work but also how to use them to deliver real, measurable value to end users.

This role is central to scaling our GenAI‑native platform for tax‑document processing, combining startup velocity with enterprise backing.

• Build and scale the ML and product infrastructure that powers intelligent tax‑document processing at production scale.
• Design and optimize inference systems, dataset pipelines, and specific logic to improve accuracy, speed, and quality as we expand to millions of documents.
• Collaborate closely with accountants and tax domain experts to deeply understand workflows, pain points, and quality thresholds, translating insights into productized ML systems.
• Integrate inference pipelines into a seamless, end‑to‑end experience that transforms how tax professionals process and interpret documents.
• Develop expert systems that encode institutional tax knowledge into scalable, maintainable software components.
• Drive experiments, measure outcomes, and iterate rapidly on core ML metrics.
• Collaborate cross‑functionally with product, engineering, and leadership to shape technical direction and influence product vision.

• 4+ years of experience in machine‑learning/AI engineering with proven end‑to‑end ownership of ML‑powered products.
• Strong track record of building systems that create direct user value, not just research prototypes or internal tooling.
• Demonstrated ability to work with large, complex datasets, optimizing for accuracy, scalability, and reliability with a solid foundation in building, measuring, and iterating on ML systems.
• Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, Tensor Flow, Keras), cloud providers such as GCP/AWS, container technologies (Docker, Kubernetes), web application development (Python‑based servers like Flask or Django), and database/storage layers (Postgres, SQL, S3/GCS).
• Experience deploying or integrating LLMs, LLM APIs, agents, and prompt engineering into production systems.
• Strong Python proficiency and hands‑on familiarity with ML infrastructure and data workflows.
• Experience in document understanding, OCR, or applied NLP.
• Exposure to financial or tax‑related data environments.
• Startup or early product experience.

• Exceptional problem‑solving ability, curiosity, and product intuition.
• Strong communication skills with the ability to engage directly with domain experts and translate complex needs into technical solutions.
• Growth trajectory demonstrated through promotions or increasing scope of responsibility.

• Applied AI / ML engineer focusing on LLMs and Knowledge Graphs: