Senior ML Encoder

Dawar Consulting; South San Francisco (CA); On-site; USD 165,000 - 248,000; Full time

Benefits offered by this job

Medical

Paid Sick Leave

401(k)

Job summary

Dawar Consulting is seeking a Senior ML Encoder Lead in South San Francisco to develop shared customer representations from longitudinal data. You will define modeling objectives, build encoder/embedding models, and deliver production-ready code while evaluating impact on downstream value.

The ideal candidate has deep expertise in representation learning and experience bringing ML models from research to production, with strong Python and modern ML framework 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.

Responsibilities

• Independently define modeling objectives and evaluation strategies.
• Build and evaluate encoder/embedding models for production-use cases.
• Develop production-ready code and data contracts; ensure reproducibility and monitoring.
• Present findings and recommendations to senior stakeholders.

Skills

Encoder/embedding models

Representation learning

Self-supervised learning

Transformers / GNNs / embeddings

Longitudinal data handling

Inductive representations

Model evaluation (time-based splits,le

Tools

Python

PyTorch

JAX

SQL

Job description

Senior ML Encoder

South San Francisco, United States | Posted on 08/26/2026

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

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 experiencepersonally training encoder or embedding modelsand designing pretraining objectives.
  
• Deep expertise inrepresentation 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 developinginductive representationsfor 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 forincremental signal, calibration, stability, drift, and subgroup performance.
  
• StrongPythonskills withPyTorch 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

• 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.