Research Scientist, Gen AI – User Representation Learning

🕒 July 27

🏄 California – Remote

🏄 California – Remote

💵 $130k - $160k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 23%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Predactiv

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2024

🤖 Artificial Intelligence

📣 Marketing

☁️ SaaS

Artificial Intelligence • Marketing • SaaS

Predactiv is an AI-powered data platform that helps brands, agencies, and data teams onboard, unify, enrich, and activate data for audience intelligence and marketing activation. The platform uses advanced AI and data science — including embeddings and agentic orchestration — to convert raw signals into behavior- and intent-based insights. Predactiv provides contextual, purchase-based, demographics, and real-time data feeds, emphasizes privacy-compliant data sourcing and security, and offers activation across channels (DSPs, clean rooms, etc. ) to support acquisition, conquesting, and loyalty strategies.

📋 Description

• 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.

🎯 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 • - PyTorch (preferred) or JAX • - Large-scale distributed data processing • - Model experimentation and evaluation • - End-to-end machine learning system development • - GPU Computing • - NVIDIA GPU architecture and CUDA programming fundamentals • - Multi-GPU and distributed training using PyTorch Distributed • - Mixed precision training (FP16/BF16/FP8) • - Profiling and optimizing GPU utilization, communication overhead, and training throughput. • Candidates should demonstrate: • - 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.