Catalyst Labs
Applied AI / ML Engineer
at
Catalyst Labs Join to apply for the
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 stand out as an agency deeply embedded in our clients recruitment operations, partnering directly with AI‑first startups, established tech companies, and enterprise innovation teams. Client: A San Francisco based startup building a GenAI‑native platform that automates one of the most complex, time‑harnessing challenges in finance and tax and turns dense tax documents into structured, usable data in minutes… The system processes K‑1s, K‑3s and sophisticated footnotes with near‑human precision, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. It serves some of the most sophisticated players in private wealth and asset management, helping them move faster and make better decisions. 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 in meaningful ownership roles, especially with document understanding and extraction. You are an exceptional builder who thrives at the intersection of real world features and AI/ML engineering, someone who not only understands how models work, but also how to use them to deliver real, 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. 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 (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 (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 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
at
Catalyst Labs Join to apply for the
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 stand out as an agency deeply embedded in our clients recruitment operations, partnering directly with AI‑first startups, established tech companies, and enterprise innovation teams. Client: A San Francisco based startup building a GenAI‑native platform that automates one of the most complex, time‑harnessing challenges in finance and tax and turns dense tax documents into structured, usable data in minutes… The system processes K‑1s, K‑3s and sophisticated footnotes with near‑human precision, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. It serves some of the most sophisticated players in private wealth and asset management, helping them move faster and make better decisions. 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 in meaningful ownership roles, especially with document understanding and extraction. You are an exceptional builder who thrives at the intersection of real world features and AI/ML engineering, someone who not only understands how models work, but also how to use them to deliver real, 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. 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 (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 (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 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