Catalyst Labs
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Applied AI / ML Engineer
role at
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, and enterprise innovation teams. We collaborate with founders, CTOs, and Heads of AI to drive the next wave of applied intelligence.
Client:
A San Francisco‑based startup building a GenAI‑native platform that automates complex finance and tax challenges. Their system processes K‑1s, K‑3s, footnotes and achieves over 99% accuracy on income lines, streamlining workflows across Excel and API integrations. Recently acquired by a global technology leader, the company now operates with the startup mindset and enterprise 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 are an exceptional builder who thrives 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
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. Fundamental understanding of building, measuring, and iterating on ML systems.
Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras), cloud providers (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.
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.
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Applied AI / ML Engineer
role at
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, and enterprise innovation teams. We collaborate with founders, CTOs, and Heads of AI to drive the next wave of applied intelligence.
Client:
A San Francisco‑based startup building a GenAI‑native platform that automates complex finance and tax challenges. Their system processes K‑1s, K‑3s, footnotes and achieves over 99% accuracy on income lines, streamlining workflows across Excel and API integrations. Recently acquired by a global technology leader, the company now operates with the startup mindset and enterprise 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 are an exceptional builder who thrives 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
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. Fundamental understanding of building, measuring, and iterating on ML systems.
Comfortable with Python and popular ML libraries (pandas, scikit‑learn, spaCy, PyTorch, TensorFlow, Keras), cloud providers (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.
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.
#J-18808-Ljbffr