Senior Lead ML Encoder [AQ-15620]

About the role

Aquent is proud to partner with a leading biotechnology company that is at the forefront of scientific discovery, dedicated to improving human health. This organization leverages cutting-edge research and innovative technologies to develop life-changing therapies. Join a team that is making a profound impact on patients’ lives globally, driving forward the future of medicine through data-driven insights and advanced analytical capabilities.

Are you a visionary machine learning leader passionate about building foundational data representations that unlock unprecedented insights? We are seeking an exceptional individual to spearhead the design and execution of a groundbreaking, comprehensive customer representation model. In this high-impact, temporary opportunity, you will be instrumental in training a dense vector embedding on extensive longitudinal transaction and interaction history. Your work will directly power next-generation generative AI and advanced analytics products across the entire enterprise, fundamentally transforming how our client understands and serves its customers. This is your chance to pioneer a first-of-its-kind solution, driving innovation and making a tangible difference in the biotechnology space.

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.

Must-Have 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.

Nice-to-Have Qualifications

• Direct experience building comprehensive customer 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).