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About the Role

Seeking a senior ML Encoder Lead to develop shared customer representations from longitudinal transaction, sales, and interaction data. The ideal candidate will independently define modeling objectives, build and evaluate encoder/embedding models, develop production-ready code, and determine whether the approach provides meaningful downstream value.

Responsibilities

• Define modeling objectives independently.
• Build and evaluate encoder/embedding models.
• Develop production-ready code.
• Determine if the approach provides meaningful downstream value.

Requirements

• Proven experience personally training encoder or embedding models and designing pretraining objectives.
• Deep expertise in representation learning, including self-supervised/contrastive learning, sequence/temporal modeling, transformers, GNNs, or recommender embeddings.
• Experience with large-scale, sparse, longitudinal event data such as transactions, clickstreams, customer journeys, or engagement histories.
• Experience developing inductive representations for entities with limited historical data.
• Strong model evaluation skills, including time-based splits, leakage detection, cold-start analysis, uncertainty, and robust baselines.
• Ability to evaluate embeddings for incremental signal, calibration, stability, drift, and subgroup performance.
• Strong Python skills with PyTorch or JAX, SQL, distributed data processing, and cloud-based model training.
• Experience taking ML models from research to production, including pipelines, data contracts, versioning, serving, monitoring, and reproducibility.
• Strong communication skills with the ability to present findings, uncertainty, and recommendations to senior stakeholders.

Skills

• Representation learning
• Self-supervised/contrastive learning
• Sequence/temporal modeling
• Transformers
• GNNs
• Recommender embeddings
• Python
• PyTorch
• JAX
• SQL
• Distributed data processing
• Cloud-based model training
• ML model productionization
• Customer-360 representations
• Behavioral embeddings
• Recommender systems
• Foundation models
• Privacy in learned representations
• Fairness in learned representations
• Re-identification risk assessment in learned representations

Location

• South San Francisco, CA

Work Type

• Long-Term Contract

Experience Level

• Senior

Benefits

• Medical
• Paid Sick Leave
• 401 (k)

About the Company

• Our client is a world leader in biotechnology and life sciences.