The roleWe're hiring our first ML engineer to lead ML and applied AI across Centralize, starting with our relationship graph: who knows who, who trusts who, and how it shifts deal by deal. You'll report to Will Wang, our co-founder and CTO, and build a new research arm from the ground floor. You'll set the research agenda, own entire systems end to end rather than one slice of a ranking model, and grow into leading the ML team as it forms around you. Your models learn from every customer deal and ship within weeks, and we'll publish what we find, with our first white papers targeted for Q1 2027.
This is not a traditional post-training research role. The work is recommender systems, knowledge graphs, and learning from past deals, applied in production. Classical ML, recsys, ranking, and graph backgrounds are all good fits, and you'll flex into applied AI when the problem calls for it.
We have two openings:
- Senior: you set the agenda, own the first systems, and grow into leading the team. Starts before the end of 2026.
- Mid-level: you go deep on large parts of the system alongside the senior hire and grow fast. Starts by the end of 2026 or in January 2027.
What you'll do- Set the research agenda for Centralize's trust graph.
- Design and build the layered knowledge graph and the recommendation systems on top of it: embeddings, retrieval, ranking, and link prediction that improve with every deal, signal, and outcome.
- Build next best actions: learn from past deals and outcomes to recommend who a rep should reach out to, why, and how.
- Drive research in production. Take ideas from first experiment to shipped feature in front of customers, and measure the impact in real deals.
- Own system design and model selection: decide when a third-party model or API is the right call and when to build in-house, and build the data, evaluation, and inference foundation that keeps it fast and reliable at enterprise scale.
- Publish what we learn in white papers and technical write-ups, present the work at conferences, and bring the findings back into the product.
- Lead complicated, long-horizon projects across engineers, writing the plan, aligning the team, and keeping the work moving for months.
- Power agents that surface the right relationship, person, or next step before a rep has to ask.
- Help build out the ML team as we grow.
What we are looking for- You're an ML engineer first, with experience building ML 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: retrieval, ranking, embeddings, and link prediction. You can flex into applied AI work when the problem calls for it.
- A track record of driving research to shipped product, and measuring whether it worked.
- Strong fundamentals in modern ML: embeddings, deep learning, and evaluation
- An extremely high level of agency, ownership, and drive. You want to go deep, own large parts of the system, and grow fast. We give you the goal, not the steps.
- Exceptional written and verbal communication. You can explain how a model behaves to engineers, customers, and the rest of the company, and write the doc that gets everyone aligned. Here, this matters as much as technical depth.
- A product-centric research mindset. You balance research depth with product deadlines and tie your work to business outcomes.
- Strong system design judgment, including build-vs-buy trade-offs between third-party models and in-house systems.
Preferred qualifications- Published papers or white papers on knowledge graphs, recommendation, or applied ML, ideally from 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.
Our Interview ProcessWe move fast, and we aim to complete this process in under two weeks. We're eager to fill this role with the right person, and we respect your time throughout.
- Founder Screen (30 min): An introductory conversation diving into your experience and background.
- ML Deep Dive: A close look at a system you built and shipped, and how you would approach the problems we tackle every day.
- Infra Conversation: A conversation with Perry, our backend and infra lead, on how you would build on our stack.
- CEO Conversation: A conversation with Rachit, our co-founder and CEO, on where the company is going.
Requirements- Remote in the United States, or hybrid in New York or San Francisco.
- US or Canadian Citizen
Benefits- Fully covered medical, dental, and vision insurance
- 401(k)
- Parental leave
- Unlimited PTO plus company holidays
- Quarterly offsites
- Equipment stipend
