Applied Research Scientist / Engineer
Location: SF Bay Area, CA, London, UK, Seattle, WA
Department: Applied Research & Engineering
Location Type: HYBRID
Employment Type: FULL_TIME
- Controllability and Features: You will leverage a toolkit spanning SFT, RL, personalization, distillation, control adapters, and more, to develop and maintain model variants purpose-built for user environments and creative partners.
- Personalization: Architect the data engine for rapid adaptation. You will leverage proprietary, vertical-specific datasets to create specialized finetunes and improve future training recipes, ensuring our models rely on data that reflects real-world use cases.
- End-User Quality: You will define and drive end-user quality – setting success metrics, building user-aligned evaluations, and iterating on the model/data/evals loop to meet strict fidelity and reliability targets in specific enterprise verticals.
- Cross-functional Collaboration: Partner closely with Product, Research, and Design to translate creative intent and user feedback into model behavior, intuitive controls, and production-ready capabilities for users and partners.
- Product-Obsessed Researcher/Engineer: You treat end users and partners as collaborators and enjoy solving specific “last mile” problems—not just optimizing public metrics.
- ML Expert: Strong ML fundamentals with deep experience in visual generative models (diffusion/transformers or related architectures). Ideal candidates also have a deep understanding of at least one: fine-tuning, personalization, domain adaptation, data curation, targeted distillation, interpretability, or human-feedback-driven refinement.
- Hands-On Builder: Strong Python and deep learning engineering skills (ideally PyTorch), comfortable moving between research prototypes and production systems.
Bonus Points
- Contributions to state-of-the-art models in image/video generation.
- Experience collaborating with creative partners (VFX, animation, film, design tools).
- Track record building workflows/tools that materially improve iteration speed and evaluation rigor.
- Familiarity with large-scale training infrastructure and distributed systems (Ray, Slurm, Kubernetes).
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