Member of Technical Staff - Product Machine Learning

Additive AI-posted 3 months ago
San Francisco, CA
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You are an exceptional builder who wants outsized responsibility and impact, and have a demonstrated history of thoughtful and pragmatic decision making. You have solved hard ML problems such as ranking, recommendation, or NER in commercial or consumer products, and an ability to collaboratively solve tough problems with minimal supervision. We should also really enjoy working together - life is too short to not genuinely be excited about your team.

  • Build the ML and product infrastructure powering our products for tax professionals, working directly with Aaron and Venky.
  • Iterate on our dataset strategy, inference systems, and tax logic to rapidly improve our core metrics such as accuracy & document coverage as we scale to millions of documents.
  • Leverage and stay up to date with cutting-edge LLMs to build robust and flexible document understanding capabilities, integrated into broader ML pipelines.
  • Delve into the gritty details of document processing and error analysis, collaborating to engineer scalable strategies for handling the full range of customer documents.
  • Engage directly with accountants to understand their needs and how we can best build a product that they absolutely love.
  • Integrate our inference pipeline into an end to end product that transforms accounting.
  • Be expected to operate with autonomy and judgement to deliver tremendous value while working with both technical and market constraints.
  • Iterate on expert systems which encode institutional knowledge of tax professionals into scalable software systems.
  • 5+ years experience working with commercial ML systems in a meaningful ownership role for what was built and why.
  • 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 (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).
  • Interest and experience with LLM prompt engineering.
  • Relocation costs covered for moving to the San Francisco area.
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