Catalyst Labs
About Us
Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We partner directly with AI‑first startups, established tech companies scaling their ML infrastructure, and enterprise innovation teams integrating AI into finance, healthcare, and logistics. Our clients include founders, CTOs, and Heads of AI driving the next wave of applied intelligence from model optimization to productized AI workflows. Client
A San Francisco‑based startup building a GenAI‑native platform that automates complex finance and tax challenges. The system turns dense tax documents into structured data in minutes, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. Recent acquisition by a global technology leader gives the team startup agility with enterprise stability and resources. Location
Union Square, San Francisco Work Type
Full Time, 5 days a week, On‑Site Compensation
Above market base + bonus + equity Visa
Sponsorship available for candidates with demonstrated brilliance and expertise. What We Are Looking For
We’re seeking an Applied AI / ML Engineer with 5+ years of experience building and scaling commercial machine learning systems, especially in document understanding and extraction. You thrive at the intersection of real‑world features and AI/ML engineering, delivering measurable value to end users. Roles & Responsibilities
Build and scale the ML and product infrastructure powering 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 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. Strong fundamental understanding of how to build, measure, and iterate on ML systems and ML‑powered enterprise or consumer products. Comfortable with Python and popular ML libraries (e.g. pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras), cloud providers such as GCP/AWS, container technologies (e.g. Docker, Kubernetes), web application development including Python‑based web servers (e.g. Flask, Django), and database and storage layers (e.g. Postgres, 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 01 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. Other Roles in Applied AI / ML in the US
Check them out here:
Applied AI / ML engineer focusing on LLMs, and Knowledge Graphs :
Catalyst Labs Careers .
#J-18808-Ljbffr
Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We partner directly with AI‑first startups, established tech companies scaling their ML infrastructure, and enterprise innovation teams integrating AI into finance, healthcare, and logistics. Our clients include founders, CTOs, and Heads of AI driving the next wave of applied intelligence from model optimization to productized AI workflows. Client
A San Francisco‑based startup building a GenAI‑native platform that automates complex finance and tax challenges. The system turns dense tax documents into structured data in minutes, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. Recent acquisition by a global technology leader gives the team startup agility with enterprise stability and resources. Location
Union Square, San Francisco Work Type
Full Time, 5 days a week, On‑Site Compensation
Above market base + bonus + equity Visa
Sponsorship available for candidates with demonstrated brilliance and expertise. What We Are Looking For
We’re seeking an Applied AI / ML Engineer with 5+ years of experience building and scaling commercial machine learning systems, especially in document understanding and extraction. You thrive at the intersection of real‑world features and AI/ML engineering, delivering measurable value to end users. Roles & Responsibilities
Build and scale the ML and product infrastructure powering 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 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. Strong fundamental understanding of how to build, measure, and iterate on ML systems and ML‑powered enterprise or consumer products. Comfortable with Python and popular ML libraries (e.g. pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras), cloud providers such as GCP/AWS, container technologies (e.g. Docker, Kubernetes), web application development including Python‑based web servers (e.g. Flask, Django), and database and storage layers (e.g. Postgres, 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 01 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. Other Roles in Applied AI / ML in the US
Check them out here:
Applied AI / ML engineer focusing on LLMs, and Knowledge Graphs :
Catalyst Labs Careers .
#J-18808-Ljbffr