Senior Lead ML Encoder

Title: Senior Lead ML Encoder
Description: ML Encoder Lead (Senior Role)
Focus: Customer Representation Learning and Encoder Development
The goal is to build the first shared learned representation of our customers - one dense vector per customer, trained on longitudinal transaction, sales and interaction history - that downstream GenAI and analytics products can reuse instead of each re-deriving its own view of the same market. The contractor will design the pretraining objective, train and evaluate the encoder, and produce the evidence that determines whether the approach continues. Evaluation is as much of the deliverable as the model. This is a hands-on senior contractor who must define the modeling objectives and evaluation design and write production code - not execute a specification handed to them.

Minimum capabilities
Has personally trained an encoder or embedding model, including designing the pretraining objective - not only consumed pre-trained embeddings or fine-tuned a published large language model
Deep expertise in representation learning: self-supervised or contrastive pretraining, sequence and temporal modeling, transformers, graph neural networks or recommender embeddings
Experience modeling large, sparse, longitudinal event data such as transactions, claims, clickstream, customer journeys or engagement histories
Experience building inductive representations, so an entity with little history can be represented from its own features rather than a lookup table
Rigorous evaluation practice: time-based splits, leakage detection, cold-start slices, transfer to held-out populations, stated uncertainty and hard baselines
Ability to judge whether an embedding carries genuine incremental signal downstream, including calibration, stability, drift and subgroup performance
Strong Python engineering with PyTorch or JAX, SQL, distributed data processing and cloud-based model training at scale
Experience carrying a model from research into production: data contracts, training pipelines, versioning, serving, monitoring and reproducibility
Ability to present findings and uncertainty credibly to senior stakeholders, and to recommend stopping an approach that is not working

Preferred
Experience with customer-360 representations, behavioral embeddings, recommender systems or foundation models over event data
Familiarity with privacy, fairness and re-identification risk in learned representations of individuals
Publications, patents or public applied work in representation learning
Any industry with large-scale behavioral event data is relevant - consumer technology, marketplaces, streaming, financial services, payments or advertising technology. Domain knowledge is not required.

Location
Bay Area based, with ability to work from the office minimum of 3 days per week

Skills: