Applied Research Scientist / Engineer
Work as a fullstack applied researcher adapting multimodal video foundation models for production. Focus on controllability, personalization, and end-user quality using SFT, RL, and data-driven refinement.
About the job
What You'll Do
You will work as a fullstack applied researcher across modeling, data, systems, and evaluation to adapt and deploy models to production.
Controllability and Features
- 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.
- 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
- 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.
Who You Are
- Product-Obsessed Researcher/Engineer: 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).
Compensation
The base pay range for this role is $200,000 – $450,000 per year.
Skills
Python, PyTorch, Diffusion Models, Transformers, Fine-Tuning, Personalization, Domain Adaptation, Data Curation, Distillation, Sft, Rl, Interpretability, Human Feedback
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