Develop shared customer representations from longitudinal transaction, sales, and interaction data. Define modeling objectives, train and evaluate encoder and embedding models, assess downstream value, and deliver production-ready machine learning systems. Responsibilities include representation learning, temporal modeling, inductive representations, robust evaluation, leakage and cold-start analysis, calibration, drift monitoring, subgroup performance, and research-to-production deployment. Present findings and recommendations to senior stakeholders while addressing privacy, fairness, and re-identification risks.
Our client, a world leader in biotechnology and life sciences, is looking for a “Senior ML Encoder”.
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.
Required Skills & Qualifications- 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.
- 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.
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