Machine Learning Engineer

About the role

ABOUT REPLAY

We are building the first-ever bank for enterprise data. There are over 35 million businesses in the US alone, many of which are sitting on an asset they don’t know they have. At Replay, we help businesses monetize their historical operating data by licensing it to the frontier AI and data labs for model training. Replay sits at the intersection of advancing the frontier of AI, while helping companies generate millions in growth capital without having to give up equity, take on debt, or lay off employees.

WHY JOIN REPLAY NOW

- We have scaled from $0 to a multi-eight-figure run rate in a matter of months

- We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund

- We are small enough that you will carry outsized responsibility and grow as quickly as the company does

- You will partner with and build for some of the fastest and most important companies in the world

- You will help build a massive, category-defining business from the ground floor

THE ROLE

Replay turns sensitive internal enterprise data into de-identified datasets without destroying the structure and meaning that make the data valuable. The data does not arrive in one clean modality. It spans messages, documents, tables, files, images, metadata, and provider-specific structures, with important context distributed across all of them.

You will improve how well our system understands and protects that data. Your initial scope will be a prioritized subset of named-entity recognition, entity and identity resolution, structured extraction, classification, semantic review, or other model-backed parts of the de-identification pipeline. We do not expect one person to be an expert in every modality. The goal is measurable improvement in the areas you own: better precision, recall, F1, high-risk coverage, and preserved data utility across the failure modes that matter.

This is an applied, production-facing ML role. You will study errors, form hypotheses, build datasets and experiments, improve or replace models, and ship the result into a live pipeline. Evaluation, reproducibility, observability, and safe releases matter because they let us identify, ship, and verify meaningful model improvements in production.

WHAT YOU'LL DO

- Own and improve NER, entity resolution, structured or tabular detection, document understanding, semantic review, or related de-identification systems

- Transform model failures and capability ceilings into a prioritized improvement roadmap

- Design active-learning loops that combine model sweeps, LLM-assisted review, clustering, and uncertainty signals to identify the examples most worth hand-labeling

- Build representative datasets and benchmarks, and use decision-relevant metrics to reveal strengths, weaknesses, uncertainty, and failure costs

- Choose and combine deterministic rules, classical ML, fine-tuning, embeddings, multimodal models, and LLM-based approaches based on the problem and evidence