Our client, a world leader in biotechnology and life sciences, is looking for a
“Senior Lead 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.
Preferred Skills
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.
