Applied AI​/ML Engineer

Seeking an Applied AI / ML Engineer with 5+ years of experience building and scaling commercial machine learning systems, especially with document understanding and extraction. This role is central to scaling a GenAI‑native platform for tax document processing, combining the velocity of a startup with the backing of an enterprise.

• 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.

• 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, Tensor Flow, 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.
• Exposure to financial or tax‑related data environments.
• Startup or 1‑person 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.