ML Encoder Lead (Senior Role)

The Fountain Group is currently seeking a ML Encoder Lead (Senior Role) for a prominent client of ours. This position will report to a client located in South San Francisco, CA . Local candidates able to have an onsite presence as needed are preferred. Well-qualified remote candidates able to overlap with PST working hours are also considered. Details for the position are as follows: Job Title: ML Encoder Lead (Senior Role) Pay Rate: Market Rate/Bid Rate hourly W2. $100-$200/hour based on qualifications. Location: South San Francisco, CA or Remote overlapping PST business hours Duration: 6 months to start Job Description Design and develop customer representation learning models using longitudinal transaction, sales, and interaction data. Define pretraining objectives and build dense customer embeddings for downstream GenAI and analytics applications. Develop, train, and evaluate encoder models using large-scale sparse event data. Design rigorous model evaluation using time-based splits, leakage detection, cold-start analysis, held-out populations, uncertainty, and baseline comparisons. Assess embedding quality through downstream signal, calibration, stability, drift, and subgroup performance. Build production-ready training and serving pipelines with reproducibility, model versioning, monitoring, and data contracts. Lead the transition of representation-learning research into scalable production systems. Present modeling results and evaluation evidence to senior stakeholders and make recommendations on model continuation or termination. Education Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field. Required Skills Hands-on experience personally training encoder or embedding models and designing pretraining objectives. Deep expertise in representation learning, including self-supervised or contrastive learning. Strong experience with sequence/temporal modeling, Transformers, Graph Neural Networks, or recommender-system embeddings. Experience modeling large-scale, sparse, longitudinal event data such as transactions, claims, clickstream, customer journeys, or engagement histories. Experience developing inductive representations for cold-start entities using entity features rather than lookup tables. Advanced Python with PyTorch or JAX. Strong SQL and distributed data processing experience. Experience with cloud-based model training at scale. Experience building production ML pipelines, model serving, versioning, monitoring, and reproducible training workflows. Preferred Skills Customer-360 representations and behavioral embeddings. Recommender systems or foundation models applied to event data. Experience addressing 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.

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