ML Encoder Lead - Senior

Job Description

Our client, a world leader in biotechnology and life sciences, is looking for a ML Encoder Lead - Senior .

Location: South San Francisco, CA

Job Duration: Long-Term Contract (Possibility Of Extension)

Company Benefits: Medical, Paid Sick Leave, 401 (k)

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.

• 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, click streams, 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.

• Customer-360 representations, behavioral embeddings, recommender systems, or foundation models.
• Knowledge of privacy, fairness, and re-identification risks in learned representations.
• Publications, patents, or public applied work in representation learning.
• Experience with large-scale behavioral data in consumer technology, marketplaces, streaming, financial services, payments, or advertising technology.