Customer Representation Learning and Encoder Development

Job Title

Pay Range: $108.68hr - $113.68hr

• Has personally trained an encoder or embedding model, including designing the pretraining objective.
• 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.
• Ability to work from the office minimum of 3 days per week.

• Build the first shared learned representation of customers using dense vectors trained on longitudinal transaction, sales and interaction history.
• Design the pretraining objective, train and evaluate the encoder, and produce evidence to determine approach viability.
• Define modeling objectives and evaluation design.
• Write production code for downstream GenAI and analytics products.
• Ensure evaluation deliverables are as robust as the model itself.

• 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.
• Experience in industries with large-scale behavioral event data such as consumer technology, marketplaces, streaming, financial services, payments or advertising technology.

• Customer-360 representations.
• Representation learning.
• PyTorch.
• JAX.
• SQL.
• Distributed data processing.
• Cloud-based model training.
• Self-supervised pretraining.
• Contrastive pretraining.
• Sequence modeling.
• Temporal modeling.
• Transformers.
• Graph neural networks.
• Recommender embeddings.
• Python engineering.

• Strong verbal and written communication skills.
• Excellent communication and presentation skills.
• Ability to communicate effectively with stakeholders.