Our client, a fast-growing AI startup recently acquired by a leading global technology company, is building next-generation machine learning systems that transform how financial professionals process and analyze complex data. The core platform leverages cutting-edge AI to automate and structure large volumes of unstructured financial documents delivering accuracy, scalability, and time savings for clients across accounting, asset management, and financial services.
Requirements
- 5+ years of experience building and deploying ML systems in production environments.
- Proven experience integrating ML models into end-user products (enterprise or consumer).
- Strong foundation in Python and modern ML tooling (e.g., PyTorch, TensorFlow, or JAX).
- Experience working with large datasets, data quality optimization, and evaluation metrics.
- Experience with LLMs, LLM APIs, or large-scale inference pipelines.
- Familiarity with financial data or document-heavy domains.
- Previous experience at high-velocity AI or data infrastructure companies.
Responsibilities
- Build and enhance ML and product infrastructure that powers AI-driven document processing systems.
- Design and iterate on datasets, inference systems, and evaluation pipelines to continuously improve performance and accuracy.
- Work directly with end users (financial professionals, accountants) to understand workflows and integrate feedback into the product roadmap.
- Collaborate cross-functionally with engineers, designers, and product leads to ship high-quality ML-powered features at scale.
- Develop expert systems that encode domain knowledge into scalable software systems.
- Contribute to an engineering culture that values rapid iteration, product impact, and measurable results.
Other
- Excellent communication skills and comfort working directly with customers or stakeholders.
- Demonstrated history of ownership and impact in a fast-paced startup or product-focused environment.
- Track record of technical leadership or mentorship within a small, high-performing team.
- Onsite in Union Square, San Francisco — collaborative and hands-on
- Sponsorship available for qualified candidates
