Senior Lead ML Encoder

Focus

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.

About the role

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
• 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
• Publishations, patents or public applied work in representation learning

Domain Knowledge

Domain knowledge is not required.

Location

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

Pay Rate Range

$80-106/hr depending on experience