Catalyst Labs
Overview
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. We collaborate with founders, CTOs, and Heads of AI to align opportunities with your technical expertise, problem-solving mindset, and long-term growth trajectory in intelligent systems. Our Client A San Francisco based startup building a GenAI-native platform that automates tax document processing, turning dense tax documents into structured data in minutes. The system processes from K-1s and K-3s to intricate footnotes with near-human precision, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. They serve sophisticated players in private wealth and asset management and are backed by seasoned founders and AI leaders from top-tier tech companies. Recently acquired by a global technology leader, the team now operates with startup speed 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 in meaningful ownership roles, with emphasis on document understanding and extraction. You are an exceptional builder who thrives at the intersection of real-world features and AI/ML engineering, capable of delivering real, measurable value to end users. This role is central as the GenAI-native platform for tax document processing scales, combining startup velocity with the backing of a large multinational firm. 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 logic to improve accuracy, speed, and quality as we expand to millions of documents. Collaborate with accountants and tax domain experts to understand workflows, pain points, and quality thresholds; translate insights into productized ML systems. Integrate inference pipelines into a seamless, end-to-end experience for tax professionals. Develop expert systems that encode institutional tax knowledge into scalable 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 product vision. Qualifications
Core Experience 4+ years of experience in machine learning / AI engineering with end-to-end ownership of ML-powered products. Track record of building systems that deliver user value, not just research prototypes. Experience with large, complex datasets; focus on accuracy, scalability, and reliability; understanding of building, measuring, and iterating on ML systems and ML-powered products. Proficiency with Python and ML libraries (pandas, scikit-learn, spaCy, PyTorch, TensorFlow, Keras); cloud providers (GCP/AWS); container tech (Docker, Kubernetes); web server frameworks (Flask, Django); databases/storage (Postgres, SQL, S3/GCS). Experience deploying or integrating LLMs, LLM APIs, agents, and prompt engineering into production systems. Strong Python skills and experience 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 to engage with domain experts and translate complex needs into technical solutions. Growth trajectory demonstrated through promotions or increasing scope of responsibility. Other
Other roles in Applied AI / ML in the US are listed for reference:
Applied AI / ML engineer focusing on LLMs, and Knowledge Graphs .
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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. We collaborate with founders, CTOs, and Heads of AI to align opportunities with your technical expertise, problem-solving mindset, and long-term growth trajectory in intelligent systems. Our Client A San Francisco based startup building a GenAI-native platform that automates tax document processing, turning dense tax documents into structured data in minutes. The system processes from K-1s and K-3s to intricate footnotes with near-human precision, achieving over 99% accuracy on income lines and streamlining workflows across Excel and API integrations. They serve sophisticated players in private wealth and asset management and are backed by seasoned founders and AI leaders from top-tier tech companies. Recently acquired by a global technology leader, the team now operates with startup speed 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 in meaningful ownership roles, with emphasis on document understanding and extraction. You are an exceptional builder who thrives at the intersection of real-world features and AI/ML engineering, capable of delivering real, measurable value to end users. This role is central as the GenAI-native platform for tax document processing scales, combining startup velocity with the backing of a large multinational firm. 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 logic to improve accuracy, speed, and quality as we expand to millions of documents. Collaborate with accountants and tax domain experts to understand workflows, pain points, and quality thresholds; translate insights into productized ML systems. Integrate inference pipelines into a seamless, end-to-end experience for tax professionals. Develop expert systems that encode institutional tax knowledge into scalable 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 product vision. Qualifications
Core Experience 4+ years of experience in machine learning / AI engineering with end-to-end ownership of ML-powered products. Track record of building systems that deliver user value, not just research prototypes. Experience with large, complex datasets; focus on accuracy, scalability, and reliability; understanding of building, measuring, and iterating on ML systems and ML-powered products. Proficiency with Python and ML libraries (pandas, scikit-learn, spaCy, PyTorch, TensorFlow, Keras); cloud providers (GCP/AWS); container tech (Docker, Kubernetes); web server frameworks (Flask, Django); databases/storage (Postgres, SQL, S3/GCS). Experience deploying or integrating LLMs, LLM APIs, agents, and prompt engineering into production systems. Strong Python skills and experience 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 to engage with domain experts and translate complex needs into technical solutions. Growth trajectory demonstrated through promotions or increasing scope of responsibility. Other
Other roles in Applied AI / ML in the US are listed for reference:
Applied AI / ML engineer focusing on LLMs, and Knowledge Graphs .
#J-18808-Ljbffr