Research Scientist, Gen AI – User Representation Learning

This is a fully remote position, open to applicants in California.

📋 Description

• Perform Applied AI Research

• Investigate and create innovative machine learning algorithms aimed at user representation learning, semantic embeddings, and applications of foundation models.

• Design, prototype, assess, and implement transformer-based generative AI solutions from research to deployment.

• Develop scalable techniques for representation learning utilizing transformers, contrastive learning, self-supervised learning, and retrieval-based architectures.

• Explore multimodal learning strategies that simultaneously model structured, behavioral, textual, and other diverse data types.

• Construct Large-Scale AI Systems

• Train and assess models leveraging extensive behavioral, transactional, social, temporal, and content datasets.

• Create embedding models, retrieval systems, vector databases, and semantic search pipelines.

• Partner with platform and infrastructure engineers to deploy high-quality AI models in production.

• Design comprehensive offline and online evaluation methodologies and establish reproducible benchmarking processes.

• Collaborate Across Teams

• Work closely with product, engineering, and domain experts to pinpoint significant research opportunities.

• Convert vague business challenges into quantifiable machine learning objectives.

• Clearly convey research results to both technical and non-technical stakeholders.

• Contribute to the long-term AI research agenda and technical strategy.

⛳️ Requirements

• PhD (completed or near completion) in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field.

• Equivalent experience in industrial research will also be taken into account.

• Strong expertise in one or more of the following areas:

• - Deep Learning

• - Representation Learning

• - Transformer architectures

• - Generative AI Models

• - Contrastive Learning

• - Self-supervised Learning

• - Embedding Models

• - Retrieval-Augmented Generation (RAG)

• - Vector Search

• - Semantic Search

• - Information Retrieval

• Proficiency in:

• - Python

• - PyTorch (preferred) or JAX

• - Large-scale distributed data processing

• - Model experimentation and evaluation

• - End-to-end machine learning system development

• - GPU Computing

• - Fundamentals of NVIDIA GPU architecture and CUDA programming

• - Multi-GPU and distributed training with PyTorch Distributed

• - Mixed precision training (FP16/BF16/FP8)

• - Profiling and optimizing GPU usage, communication overhead, and training throughput.

• Candidates should exhibit:

• - Strong scientific rigor

• - Capability to establish meaningful baselines prior to pursuing more intricate models

• - Well-structured experiments and reproducible evaluations

• - Data-driven decision-making

• - Intellectual curiosity and independent problem-solving abilities.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Generous paid time off and holiday policies.

• Opportunities for professional development and continuous learning.

• Flexible work hours and remote work options.