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Research Engineer (Applied Research)

Job description

Applied Research Engineer
$175,000 - $200,000 + Market Leading Equity + Benefits + PTO
Palo Alto, CA - On-site

Are you a builder at heart with a passion for cutting-edge generative AI? Do you thrive at the intersection of research and real-world product impact?

This is an incredible opportunity to join a fast-moving AI startup that's developing the next generation of multimodal generative technologies. Backed by top-tier investors ($xxx million in funding) and powered by a world-class technical team, they're building advanced models that push the frontier of what's possible in vision and language.

I'm working with a well-funded Palo Alto-based AI startup expanding its Applied Research team. They're looking for Research Engineers to help prototype, fine-tune, and productionize breakthrough generative features-bringing new capabilities to life across their platform.

You'll work closely with research scientists and product teams to take ideas from concept to real-world deployment. This is a hands-on, high-impact role ideal for someone who wants to experiment, optimize, and ship features that touch thousands (or millions) of users.

This is a rare opportunity to join a world-class applied research team, work with massive GPU clusters, and help shape the future of multimodal AI, whilst benefiting from an excellent equity and compensation, and supercharging your career progression.

The Role

  • Collaborate with Research and Product teams to design novel generative AI features for integration into user-facing products.
  • Fine-tune and evaluate models, contributing to dataset development, optimization strategies, and benchmarking tools.
  • Build internal tooling to assess performance, debug failure modes, and drive improvements in model output and reliability.
  • Work with large-scale GPU resources and experiment with the latest in multimodal model architectures.
  • On-site in Palo Alto, CA


Ideal Candidate

  • Strong background in Python and PyTorch with hands-on ML project experience.
  • Experience working with visual generative AI, particularly diffusion models and Transformer-based architectures.
  • Passion for multimodal research and a deep curiosity for exploring novel applications.
  • Open-source contributions (e.g., Stable Diffusion forks or model training pipelines)