Catalyst Labs
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Applied AI / ML Engineer
role at
Catalyst Labs .
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, and enterprise innovation teams to integrate AI into traditional domains.
Client Our client is a San Francisco‑based startup building a GenAI‑native platform that automates complex finance and tax workflows. They turn dense tax documents into structured data in minutes, achieving over 99% accuracy on income lines. The team scales data‑driven products to wholesale investment clients and is now applying GenAI to transform financial data understanding.
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 qualified candidates.
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, particularly 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 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 Core Experience
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.
Experience with large, complex datasets, optimizing for accuracy, scalability, and reliability; deep understanding of how to build, measure, and iterate on ML systems.
Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras). Familiar with cloud providers such as GCP/AWS, container technologies (Docker, Kubernetes), web application development (Flask, 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 first‑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
role at
Catalyst Labs .
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, and enterprise innovation teams to integrate AI into traditional domains.
Client Our client is a San Francisco‑based startup building a GenAI‑native platform that automates complex finance and tax workflows. They turn dense tax documents into structured data in minutes, achieving over 99% accuracy on income lines. The team scales data‑driven products to wholesale investment clients and is now applying GenAI to transform financial data understanding.
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 qualified candidates.
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, particularly 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 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 Core Experience
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.
Experience with large, complex datasets, optimizing for accuracy, scalability, and reliability; deep understanding of how to build, measure, and iterate on ML systems.
Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras). Familiar with cloud providers such as GCP/AWS, container technologies (Docker, Kubernetes), web application development (Flask, 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 first‑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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