Senior Lead ML Encoder

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

Benefits offered by this job

Health insurance

Vision insurance

Dental insurance

Paid sick leave

401(k) with matching

Training opportunities

Job summary

Aquent partners with a major Biotechnology company to find a Senior Lead ML Encoder for a temporary opportunity. You will lead the design and execution of a first-of-its-kind customer-360 representation model—training a dense vector embedding on longitudinal transaction and interaction history to power GenAI and analytics products across the enterprise.

You will collaborate with data scientists and product teams, implement robust evaluation and production pipelines in PyTorch or JAX, and present

Qualifications

• H hands-on experience designing pretraining objectives and training encoder/embedding models from scratch.
• Deep expertise in representation learning techniques (contrastive learning, temporal sequences, Transformers, GNNs).
• Proven track record modeling large, sparse longitudinal event data.
• Experience building inductive representations generalizing with minimal history.
• Strong Python engineering with PyTorch or JAX and distributed data frameworks.

Responsibilities

• Design pretraining objectives and build representations from longitudinal data.
• Develop, train, and evaluate encoder models using PyTorch or JAX for scalable distributed processing.
• Establish robust evaluation frameworks with time-based splits and leakage detection.
• Write production-grade Python code to move models from research to deployment.
• Assess embedding signal by calibration, drift, and subgroup performance.
• Present findings and recommendations to senior stakeholders.

Skills

Pretraining objectives design

Representation learning

PyTorch

JAX

Python

Longitudinal data modeling

Model evaluation

Tools

SQL

Distributed training

Cloud training at scale

Job description

Aquent is partnering with a major Biotechnology company to find a Senior Lead ML Encoder for a Temporary opportunity. In this high-impact role, you will lead the design and execution of a first-of-its-kind customer-360 representation model—training a dense vector embedding on longitudinal transaction and interaction history to power downstream GenAI and analytics products across the enterprise.

To be considered for this role, you must:

• Be authorized to work in the United States
• Not require sponsorship of any kind for the duration of the assignment
• Be able to work on a W-2 basis (C2C or 1099 is not permitted for this position)

Key Responsibilities

• Design pretraining objectives and build self-supervised or contrastive representations using longitudinal customer transaction, sales, and interaction data
• Develop, train, and evaluate end-to-end encoder models utilizing PyTorch or JAX, ensuring scalable distributed data processing
• Establish robust, rigorous evaluation frameworks, including time-based splits, leakage detection, cold-start slices, and transferability to held-out populations
• Write clean, production-grade Python code to transition models from research into reliable, versioned, and monitored training and serving pipelines
• Judge downstream embedding signal by measuring calibration, stability, drift, and subgroup performance to determine if models deliver incremental value
• Present research findings, model capabilities, and trade-offs to senior stakeholders, providing data-driven recommendations on whether to advance or halt specific modeling approaches

Requirements & Qualifications

• Hands-on experience designing pretraining objectives and training encoder/embedding models from scratch (not just fine-tuning or consuming pretrained models)
• Deep expertise in representation learning techniques, such as contrastive learning, temporal sequence modeling, Transformers, Graph Neural Networks (GNNs), or recommender embeddings
• Proven track record modeling large, sparse, longitudinal event data (e.g., transactions, customer journeys, clickstreams, or engagement histories)
• Experience building inductive representations that generalize to entities with minimal history using their own features rather than static lookup tables
• Exceptional evaluation hygiene, including time-based splitting, leakage detection, uncertainty estimation, and hard baseline comparisons
• Strong engineering expertise in Python with PyTorch or JAX, SQL, distributed data frameworks, and cloud-based training at scale
• Demonstrated ability to carry models into production, including setting up data contracts, versioning, serving pipelines, and monitoring
• Strong communication skills with the confidence to articulate uncertainty and recommend stopping viable approaches when signal is insufficient

Preferred Qualifications

• Direct experience building Customer-360 representations, behavioral embeddings, or foundation models over event data
• Familiarity with privacy, fairness, and re-identification risks in learned representations of individuals
• Relevant publications, patents, or public applied work in representation learning
• Prior experience working in high-volume behavioral data environments (e.g., consumer tech, financial services, payments, adtech, or marketplaces)

The target hiring compensation range for this role is $91.09–$101.21/hr. Compensation is based on several factors including, but not limited to education, relevant work experience, relevant certifications, and location.

About Aquent Talent: Aquent Talent connects the best talent in marketing, creative, and design with the world’s biggest brands. Our eligible talent get access to amazing benefits like subsidized health, vision, and dental plans, paid sick leave, and retirement plans with a match. We also offer free online training through Aquent Gymnasium. More information on our awesome benefits!

Aquent is an equal-opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics. We’re about creating an inclusive environment—one where different backgrounds, experiences, and perspectives are valued, and everyone can contribute, grow their careers, and thrive.