As a GenAI Research Scientist/Engineer, you will lead efforts in adapting large-scale generative models, focusing on personalizing outputs, conducting applied research, and collaborating with cross-functional teams on production integration.
About us:
The role:
What you’ll do:
You may be a strong fit if you:
Logistics:
At Phota Labs, we’re building visual GenAI that helps people capture, express, and relive their memories — in ways that feel effortless, personal, and emotionally resonant. Our core technology enables personalized image generation that faithfully reflects who you are and the moments you experienced. Our first goal is to bring visual GenAI into everyday photography.
We're a small team of researchers, engineers, and designers who have always been at the forefront of how people capture, edit, and share images and videos. We build with our hands and hearts. We believe GenAI is the next shift for photography, and are seeking builders who share this vision — people like us, like you. We're just getting started!
As a GenAI research Scientist/Engineer, you'll lead the post-training and adaptation of large-scale generative models. But this isn't about replicating what others have done - building yet another large and generic model with massive data. Your focus will be on making these models personal: adapting them to generate images that reflect individual identity and context through techniques like fine-tuning, conditioning, personalization, or novel methods you invent.
- Train, adapt, and fine-tune large-scale generative models with controls beyond text input.
- Rapidly prototype ideas from the latest research, or your own, across model architecture, personalization, conditioning, and training strategies.
- Conduct applied research on real-world challenges like content consistency, identity preservation, and contextual alignment.
- Design and run rigorous evaluations to inform tradeoffs, validate hypotheses, and guide model improvements.
- Collaborate closely with our ML/CV teams to productionize your research and integrate it into user-facing pipelines.
- Have trained and fine-tuned large-scale diffusion models for image or video, especially with controls beyond plain text.
- Are experienced with post-training techniques and have worked across different types of generative model architectures.
- Know how to evaluate generative models rigorously: from quantitative metrics to subjective quality.
- Are highly proficient in PyTorch and deeply familiar with transformer architectures and modern neural networks.
- Have strong experience with large-scale training techniques — including FSDP, low-precision training, and model/data parallelism.
- Have demonstrated research depth through impactful publications or shipped GenAI systems.
- Take ownership, care about solving real problems (not just writing papers), and work well with cross-functional teams to bring ideas into production.
- This role is based in San Jose, where we work in person. We believe the best ideas come from being in the same room.
- We sponsor visas. We are committed to working through the process together for the right candidates. If you're currently outside the US, we're also committed to helping you relocate to the US throughout this process.
- We offer generous health, dental, and vision coverage, unlimited PTO, paid parental leave, and relocation support as needed.
- Don't meet every single qualification? That’s okay — we care more about your trajectory than checking every box. If the role excites you and the mission resonates, we'd love to hear from you.
Note: In the event your application is successful and an offer of employment is made to you, any offer of employment will be conditional on the results of a background check, performed by a third party acting on our behalf.
Top Skills
Computer Vision
Diffusion Models
Generative Models
Machine Learning
Neural Networks
PyTorch
Phota Labs San Jose, California, USA Office
San Jose, CA, United States
Similar Jobs
.jpeg)
Artificial Intelligence • Hardware • Information Technology • Machine Learning
The Director of Cost Planning at Micron Technology leads forecasting, analysis, and reporting of global standard costs and manufacturing variances, working closely with various finance and operations departments to optimize cost performance.
Top Skills:
Erp SystemsSAP
Artificial Intelligence • Hardware • Information Technology • Machine Learning
As a Member of Technical Staff, you will design and microarchitect high-performance memory solutions for AI, optimizing parameters like power and area.
Top Skills:
C/C++PythonSystemverilog
Artificial Intelligence • Hardware • Information Technology • Machine Learning
The role involves leading strategic customer engagements in the Americas for Micron's storage solutions, managing revenue and relationships with enterprise clients while navigating complex sales processes.
Top Skills:
Ai SystemsCloud HardwareDatacenter TechnologySemiconductor Technology
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
