The company is solving the problem of automating the processing of complex tax documents in finance, turning dense tax documents into structured, usable data with near-human precision. The goal is to streamline workflows across Excel and API integrations, enabling faster and more accurate decision-making in private wealth and asset management.
Requirements
- 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 (e.g. pandas, scikit-learn, spaCy, pytorch, tensorflow, keras), cloud providers such as GCP/AWS, container technologies (e.g. Docker, Kubernetes), web application development including Python-based web servers (e.g. Flask, Django), and database and storage layers (e.g. 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.
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.
Other
- Startup or 01 product experience.
- 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.
- Visa sponsorship available for candidates with demonstrated brilliance and expertise.
