Founding Machine Learning Engineer

Centralize

About The Position

Centralize is hiring its first machine learning engineer to lead machine learning and applied AI, beginning with the company’s relationship graph. The role will establish a research function, set its agenda, and own end-to-end production systems rather than a single component of a model. The senior opening is expected to grow into leading the ML team as it forms. The work applies recommender systems, knowledge graphs, and learning from past deals in production; the role may also involve applied AI where appropriate. Models learn from customer deals and are shipped on a rapid cycle. The company plans to publish findings, with its first white papers targeted for Q1 2027. There are senior and mid-level openings. The senior hire will set the agenda and own the first systems; the mid-level hire will work alongside the senior hire on substantial parts of the system. The role reports to co-founder and CTO Will Wang. Candidates may work remotely in the United States or in a hybrid arrangement in New York or San Francisco, and must be a US or Canadian citizen.

Requirements

Experience building machine learning systems that run in production at scale; recommender systems, ranking, or graph-based models are especially relevant.
Hands-on experience building recommender systems, knowledge graphs, or people and social graphs, including retrieval, ranking, embeddings, and link prediction.
Track record of driving research into shipped products and measuring its effectiveness.
Strong fundamentals in modern machine learning, including embeddings, deep learning, and evaluation.
High agency, ownership, and drive, with a willingness to work deeply, own large parts of a system, and grow quickly.
Exceptional written and verbal communication, including explaining model behavior to engineers, customers, and colleagues and writing documents that align a team.
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 trade-offs between third-party models and in-house systems.
US or Canadian citizenship.
For the senior opening, the start is before the end of 2026. For the mid-level opening, the start is by the end of 2026 or in January 2027.

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.
An MS or PhD 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 that improve with deals, signals, and outcomes.
Build next-best-action recommendations using past deals and outcomes to advise sales representatives whom to contact, why, and how.
Take research ideas from initial experiments through shipped customer-facing features and measure their impact in real deals.
Own system design and model selection, including deciding when to use third-party models or APIs versus building in-house, and build the data, evaluation, and inference foundation 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 surface relevant relationships, people, or next steps proactively for sales representatives.
Help build the ML team as the company grows.