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.

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.

If interested, please share your updated resume at hr@dawarconsulting.com/saurav@dawarconsulting.com.

Compétences requises

Representation learning (self-supervised, contrastive, transformers, GNNs, recommender embeddings)Training encoder/embedding models and designing pretraining objectivesSequence/temporal modeling for longitudinal event dataWorking with large-scale, sparse, longitudinal event data (transactions, clickstreams, customer journeys)Developing inductive representations for entities with limited historical dataModel evaluation and validation (time-based splits, leakage detection, cold-start analysis, uncertainty, robust baselines)Embedding evaluation (incremental signal, calibration, stability, drift, subgroup performance)Python programmingDeep learning frameworks (PyTorch or JAX)SQLDistributed data processingCloud-based model trainingML productionization (pipelines, data contracts, versioning, serving, monitoring, reproducibility)Stakeholder communication and presentation to senior leadershipKnowledge of privacy, fairness, and re-identification risks in learned representations

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