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 scaling ML infrastructure, and enterprise innovation teams integrating AI into traditional domains such as finance, healthcare, and logistics.
Our Client
Our client is a San Francisco based startup building a GenAI‑native platform that automates finance and tax document processing. The platform turns dense tax documents into structured, usable data in minutes, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations.
Location:
Union Square, San Francisco
Work type:
Full Time, 5 days a week, On‑Site
Compensation:
above market base + bonus + equity
Visa:
sponsorship available
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.
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 such as GCP/AWS, container technologies (Docker, Kubernetes), web application development including Python‑based web servers (Flask, Django), and database and storage layers (PostgreSQL, 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‑stage 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 scaling ML infrastructure, and enterprise innovation teams integrating AI into traditional domains such as finance, healthcare, and logistics.
Our Client
Our client is a San Francisco based startup building a GenAI‑native platform that automates finance and tax document processing. The platform turns dense tax documents into structured, usable data in minutes, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations.
Location:
Union Square, San Francisco
Work type:
Full Time, 5 days a week, On‑Site
Compensation:
above market base + bonus + equity
Visa:
sponsorship available
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.
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 such as GCP/AWS, container technologies (Docker, Kubernetes), web application development including Python‑based web servers (Flask, Django), and database and storage layers (PostgreSQL, 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‑stage 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