Senior Data Scientist - LLMs, RAG & Multimodal AI (Remote | Immediate joiner)

Proximity Works

Senior Data Scientist

Role Summary

hands-on applied science role

What You’ll Do

• Design, fine-tune, and optimize LLMs

  for applied multimodal generation use cases.

• Build and productionize RAG pipelines

  that combine embedding-based search, metadata filtering, and LLM-driven re-ranking/summarization.
• Apply

  prompt engineering, RAG techniques, and model distillation

  to improve grounding, reduce hallucinations, and ensure output reliability.
• Define and implement

  evaluation metrics

  across semantic search (nDCG, Recall@K, MRR) and generation quality (grounding accuracy, hallucination rate).

• Optimize inference pipelines

  for latency-sensitive use cases with strategies like token budgeting, prompt compression, and sub-100ms response targets.
• Train and adapt models via

  transfer learning, LoRA/QLoRA, and checkpoint reloading

  , ensuring robust deployment in production environments.
• Collaborate with product and research teams to explore

  innovative multimodal integrations

  for user-facing applications.

What Success Looks Like

• Deployment of

  production-ready LLM + RAG pipelines

  powering global-scale search and discovery applications.
• Demonstrable improvements in

  grounding accuracy and hallucination reduction

  across deployed systems.
• Consistent delivery of

  sub-100ms inference latency

  for generation workloads.
• Adoption of

  rigorous evaluation metrics

  that drive continuous model improvement.
• Effective cross-functional collaboration with engineering, product, and research teams.

What You’ll Need

• Strong background in

  NLP, machine learning, and multimodal AI

  .
• Proven hands-on experience in

  LLM fine-tuning, RAG, distillation, and evaluation of foundation models

  .
• Expertise in semantic search and retrieval pipelines (e.g.,

  FAISS, Weaviate, Vespa, Pinecone

  ).
• Demonstrated ability to

  deploy models at scale

  , including distributed inference setups.
• Solid understanding of

  evaluation frameworks

  for ranking, retrieval, and generation.
• Proficiency in

  Python

  ,

  PyTorch/TensorFlow

  , and modern ML toolkits.
• Experience in

  multimodal AI

  (bridging text, vision, or speech with LLMs).
• Track record of shipping

  latency-sensitive AI products

  .
• Strong communication skills and the ability to collaborate with

  cross-functional global teams

  .

Success Traits

• Research-to-production mindset

  – thrives in moving cutting-edge methods into real-world systems.

• High ownership

  – proactive in identifying performance bottlenecks and solving them.

• Analytical rigor

  – strong in defining and applying evaluation metrics.

• Curiosity & creativity

  – eager to push the boundaries of LLMs, RAG, and multimodal AI.

• Collaborative spirit

  – effective working across global engineering and product teams.

Why Join Proximity Works

• Work directly on

  frontier AI problems

  shaping the future of search, sports, streaming, and media.
• Be part of a

  global-first, high-performance AI research and engineering culture

  .
• Competitive compensation aligned with global markets, with remote-first flexibility.
• Annual global off-sites with Proxonauts from San Francisco, Dubai, India, and beyond.
• High autonomy, direct accountability, and the opportunity to

  ship multimodal AI systems at global scale

  .