Research Scientist, Gen AI & User Representation Learning

Predactiv•Palo Alto, CA

30d

About The Position

We are looking for an exceptional Research Scientist to develop next-generation AI technologies, focusing on user representation learning, semantic understanding, and generative AI applications. You will conduct applied research that advances representation learning, multimodal understanding, and transformer-based modeling while working closely with engineering teams to translate research into production systems. The ideal candidate combines strong scientific thinking with practical engineering skills and enjoys solving challenging problems using large-scale real-world data.

Requirements

PhD (completed or near completion) in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative discipline. Equivalent industrial research experience will also be considered.
Strong background in one or more of the following: 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.
Experience with Python
Experience with PyTorch (preferred) or JAX
Experience with Large-scale distributed data processing
Experience with Model experimentation and evaluation
Experience with End-to-end machine learning system development
Experience with GPU Computing
Experience with NVIDIA GPU architecture and CUDA programming fundamentals
Experience with Multi-GPU and distributed training using PyTorch
Experience with Distributed Mixed precision training (FP16/BF16/FP8)
Experience with Profiling and optimizing GPU utilization, communication overhead, and training throughput
Strong scientific rigor
Ability to establish meaningful baselines before pursuing more complex models
Well-designed experiments and reproducible evaluations
Data-driven decision making
Intellectual curiosity and independent problem solving

Nice To Haves

5 years of industrial or applied research experience preferred (including internships).
Combine research excellence with strong engineering execution.
Enjoy working with ambiguous, real-world business problems.
Can independently drive projects from idea to production.
Thrive in highly collaborative, cross-functional environments.
Have excellent written and verbal communication skills.
Are passionate about building practical generative AI systems that create measurable business impact.

Responsibilities

Conduct Applied AI Research
Research and develop novel machine learning algorithms for user representation learning, semantic embeddings, and foundation-model applications.
Design, prototype, evaluate, and deploy transformer-based generative AI solutions from research through deployment.
Develop scalable representation learning techniques using transformers, contrastive learning, self-supervised learning, and retrieval-based architectures.
Investigate multimodal learning approaches that jointly model structured, behavioral, textual, and other heterogeneous data.
Build Large-Scale AI Systems
Train and evaluate models using large-scale behavioral, transactional, social, temporal, and content datasets.
Design embedding models, retrieval systems, vector databases, and semantic search pipelines.
Collaborate with platform and infrastructure engineers to deploy production-quality AI models.
Design rigorous offline and online evaluation methodologies and establish reproducible benchmarking pipelines.
Collaborate Across Teams
Work closely with product, engineering, and domain experts to identify impactful research opportunities.
Translate ambiguous business problems into measurable machine learning objectives.
Communicate research findings clearly to both technical and non-technical audiences.
Contribute to the long-term AI research roadmap and technical strategy.