Founding Machine Learning Engineer

Centralize•San Francisco, CA

22h•Hybrid

About The Position

Centralize is hiring its first Machine Learning Engineer to lead machine learning and applied AI across the company, beginning with its relationship graph. The role will establish a new research arm, set its research agenda, and build end-to-end production systems for relationship intelligence. The work focuses on recommender systems, knowledge graphs, learning from past deals, and applied AI—not traditional post-training research. Models learn from customer deals and are expected to ship within weeks, with findings shared through white papers, technical write-ups, and conference presentations. There are two openings: a Senior role that sets the agenda, owns the first systems, and grows into leading the team; and a Mid-level role that develops large parts of the system alongside the senior hire. The Senior role starts before the end of 2026; the Mid-level role starts by the end of 2026 or in January 2027. The position reports to co-founder and CTO Will Wang. The role is remote in the United States or hybrid in New York or San Francisco, and requires US or Canadian citizenship.

Requirements

Experience building machine learning systems that run in production at scale, ideally recommender systems, ranking, or graph-based models.
Hands-on experience building recommender systems, knowledge graphs, or people and social graphs, including retrieval, ranking, embeddings, and link prediction.
Ability to work on applied AI when the problem calls for it.
Track record of taking research through to a shipped product and measuring its effectiveness.
Strong modern machine learning fundamentals, including embeddings, deep learning, and evaluation.
High agency, ownership, and drive, with willingness to work deeply, own substantial parts of systems, and grow quickly.
Exceptional written and verbal communication, including explaining model behavior to engineers, customers, and colleagues and writing documents that align teams.
Product-centric research mindset that balances research depth with product deadlines and connects work to business outcomes.
Strong system design judgment, including build-versus-buy decisions involving third-party models and in-house systems.
US or Canadian citizenship.
Ability to work remotely in the United States or in a hybrid arrangement in New York or San Francisco.

Nice To Haves

Published papers or white papers on knowledge graphs, recommendation, or applied ML, ideally based on work that shipped.
Experience with people or social graphs at scale, such as friend or connection recommendations, identity resolution, or entity matching.
A master's or Ph.D. in a quantitative field, or equivalent research experience.

Responsibilities

Set the research agenda for Centralize’s trust graph.
Design and build a layered knowledge graph and recommendation systems, including embeddings, retrieval, ranking, and link prediction.
Build next-best-action capabilities that learn from past deals and outcomes to recommend who a sales representative should contact, why, and how.
Take research ideas from initial experiments through production features, and measure their impact in real deals.
Own system design and model selection, including decisions about using third-party models or APIs versus building in-house.
Build the data, evaluation, and inference foundation needed for fast, reliable operation at enterprise scale.
Publish findings in white papers and technical write-ups, present work at conferences, and apply findings to the product.
Lead complex, long-horizon projects across engineers by writing plans, aligning the team, and maintaining progress over months.
Power agents that proactively surface relevant relationships, people, or next steps.
Help build and grow the ML team.
For the senior opening, establish the agenda, own the first systems, and grow into leading the team.
For the mid-level opening, develop major parts of the system alongside the senior hire and grow quickly.