Senior ML Embedding Architect - Foundation Models (Contract)

Skill; San Francisco (CA); On-site; USD 125,000 - 139,000; Full time

Benefits offered by this job

subsidized health, vision, and dental

paid sick leave

retirement plans with a match

free online training through AquentGym

Job summary

Aquent Talent partners with leading biotech firms to advance data-driven medicine. This temporary, high-impact role focuses on training dense embeddings from longitudinal histories to power next‑gen generative AI and analytics across the enterprise.

You will design pretraining objectives, build encoder models, and implement robust evaluation while collaborating with stakeholders to determine when to proceed or pivot.

Qualifications

• Hands-on experience designing pretraining objectives and training encoder/embedding models from scratch.
• Deep expertise in representation learning: contrastive learning, temporal sequence modeling, Transformers, Graph Neural Networks (GNNs), or recommender embeddings.
• Proven track record modeling large, sparse, longitudinal event data.
• Experience building inductive representations that generalize to entities with minimal history.
• Strong evaluation hygiene, including time-based splitting, leakage detection, uncertainty estimation.
• Strong Python engineering with PyTorch or JAX, SQL, distributed data frameworks, and cloud-based training at scale.
• Demonstrated ability to carry models into production with data contracts, versioning, serving pipelines, and monitoring.
• Strong communication skills to articulate uncertainty and recommend stopping approaches when signal is insufficient.

Responsibilities

• Design pretraining objectives and build self-supervised or contrastive representations using longitudinal data.
• Develop, train, and evaluate end-to-end encoder models using PyTorch or JAX, with scalable distributed processing.
• Establish evaluation frameworks including time-based splits, leakage checks, and transferability assessments.
• Write clean production-grade Python to move models from research to production pipelines.
• Judge embedding signal by metrics on calibration, drift, and subgroup performance for incremental value.
• Present findings and trade-offs to senior stakeholders with data-driven recommendations.

Skills

Pretraining objectives

Encoder embeddings

Representation learning

Longitudinal data

Time-based evaluation

Python (PyTorch/JAX)

SQL & distributed data

Production ML

Communication skills

Tools

PyTorch

JAX

SQL

Distributed data

Cloud training

Job description

Aquent Talent partners with leading biotech firms to advance data-driven medicine. This temporary, high-impact role focuses on training dense embeddings from longitudinal histories to power next‑gen generative AI and analytics across the enterprise.

You will design pretraining objectives, build encoder models, and implement robust evaluation while collaborating with stakeholders to determine when to proceed or pivot.