Catalyst Labs
Applied AI / ML Engineer – Catalyst Labs
Join Catalyst Labs, a leading talent agency specialized in Applied AI, Machine Learning, and Data Science, as an
Applied AI / ML Engineer .
About Catalyst Labs Catalyst Labs partners with AI‑first startups, established tech companies, and enterprise innovation teams to embed AI into recruitment operations and product development. We focus on model optimization, productized AI workflows, and long‑term growth for technical talent.
Our Client We are recruiting for a San Francisco‑based GenAI‑native startup that automates complex finance and tax document processing. The platform transforms K‑1s, K‑3s, and footnotes into structured data with >99% accuracy, serving high‑net‑worth private wealth and asset‑management clients. They are backed by seasoned founders and AI leaders from top tech firms and recently acquired by a global technology leader.
Location & Work Type Union Square, San Francisco – Full‑time, on‑site, 5 days a week.
Compensation & Visa Above‑market base salary + bonus + equity; visa sponsorship available for qualified candidates.
What We Are Looking For We seek an experienced Applied AI / ML Engineer with 5+ years of building and scaling commercial ML systems, especially in document understanding and extraction. You thrive at the intersection of real‑world features and ML engineering, driving measurable value to end users.
Roles & Responsibilities
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.
Qualifications
4+ years of 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.
Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras), cloud providers (GCP, AWS), container technologies (Docker, Kubernetes), web frameworks (Flask, Django), and databases (PostgreSQL, SQL, S3/GCS).
Experience deploying or integrating LLMs, LLM APIs, agents, and prompt engineering into production systems.
Experience in document understanding, OCR, or applied NLP.
Exposure to financial or tax‑related data environments.
Startup or product experience.
Soft Skills
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.
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Applied AI / ML Engineer .
About Catalyst Labs Catalyst Labs partners with AI‑first startups, established tech companies, and enterprise innovation teams to embed AI into recruitment operations and product development. We focus on model optimization, productized AI workflows, and long‑term growth for technical talent.
Our Client We are recruiting for a San Francisco‑based GenAI‑native startup that automates complex finance and tax document processing. The platform transforms K‑1s, K‑3s, and footnotes into structured data with >99% accuracy, serving high‑net‑worth private wealth and asset‑management clients. They are backed by seasoned founders and AI leaders from top tech firms and recently acquired by a global technology leader.
Location & Work Type Union Square, San Francisco – Full‑time, on‑site, 5 days a week.
Compensation & Visa Above‑market base salary + bonus + equity; visa sponsorship available for qualified candidates.
What We Are Looking For We seek an experienced Applied AI / ML Engineer with 5+ years of building and scaling commercial ML systems, especially in document understanding and extraction. You thrive at the intersection of real‑world features and ML engineering, driving measurable value to end users.
Roles & Responsibilities
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.
Qualifications
4+ years of 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.
Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras), cloud providers (GCP, AWS), container technologies (Docker, Kubernetes), web frameworks (Flask, Django), and databases (PostgreSQL, SQL, S3/GCS).
Experience deploying or integrating LLMs, LLM APIs, agents, and prompt engineering into production systems.
Experience in document understanding, OCR, or applied NLP.
Exposure to financial or tax‑related data environments.
Startup or product experience.
Soft Skills
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.
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