Research Scientist, Gen AI & User Representation Learning

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.

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.

Education

• 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.

Technical Expertise

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

Research Mindset

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

• 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.
• 5 years of industrial or applied research experience preferred (including internships).

For compensation, we set standard ranges based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.