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Senior Lead ML Encoder
Job Location:
Job Location:
South San Francisco, CA - USA
Salary:
Not provided by the employer
Salary:
Posted:
26 August 2026 (9 hours ago)
Posted:
Application Deadline:
23 November 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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.
Required Skills:
Primary immune-cell isolation In-vitro immunological and cellular assays Cell engineering flow cytometry
