Jobgether
Senior Applied Research Engineer - Video

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Senior Applied Research Engineer - Video
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied Research Engineer - Video based in United Kingdom.
As a Senior Applied Research Engineer, you will help build the next generation of production-grade foundation models for human-centric video generation.
You will work at the intersection of generative AI research, large-scale distributed systems, and production engineering.
Your work will focus on developing realistic, controllable, and expressive video generation models that can operate reliably at scale.
You will own research and engineering projects end to end, translating hypotheses and experiments into measurable product impact.
The role combines advanced modeling, distributed training, evaluation, inference optimization, and rigorous experimentation.
You will operate in a highly technical, high-ownership environment where research is expected to move quickly toward real-world deployment.
Your contributions will directly influence AI-powered video products used by businesses around the world.
Accountabilities
- Develop and scale latent video diffusion models designed for human-centric video generation.
- Design advanced conditioning mechanisms that improve control over elements such as pose, emotion, scripts, and camera movement while maintaining high visual fidelity.
- Lead end-to-end applied research and engineering projects, from developing hypotheses and running experiments through to production implementation and measurable impact.
- Develop and optimize distributed training strategies using technologies such as DDP, FSDP, DeepSpeed, and sequence parallelism.
- Improve training stability and efficiency across large-scale, multi-GPU and multi-node environments while working within real-world compute constraints.
- Design robust evaluation frameworks combining automated metrics with structured human evaluation to assess model quality and performance.
- Optimize model inference for low latency, high resolution, scalability, and cost efficiency in production environments.
- Run controlled experiments, ablations, and parallel research hypotheses to identify high-value signals and guide modeling decisions.
- Establish and maintain strong engineering practices around reproducibility, experiment tracking, CI/CD, monitoring, and production reliability.
- Translate research findings into practical improvements for production-grade generative video systems.
- Collaborate actively with researchers, engineers, and cross-functional teams while maintaining a high degree of individual ownership.
- Move quickly between promising research directions, identifying low-signal approaches early and prioritizing work based on measurable outcomes.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Requirements
- Strong professional experience training deep learning models at scale, ideally in a research or production environment.
- Strong programming skills in Python and hands-on expertise with PyTorch.
- Practical experience working with diffusion models, with image-generation experience required and video-generation experience strongly preferred.
- Proven experience with large-scale multi-GPU and multi-node model training.
- Strong understanding of distributed training frameworks and techniques such as DDP, FSDP, DeepSpeed, or comparable technologies.
- Ability to design controlled experiments, analyze noisy or ambiguous results, and make scientifically grounded modeling decisions.
- Experience with video diffusion models is an advantage.
- Experience with avatar generation, synthetic humans, or other human-centric generative AI applications is a plus.
- Familiarity with world models, interactive models, GANs, or VAEs is desirable.
- Experience optimizing inference systems for production deployment is an advantage.
- Strong understanding of CUDA and experience working within modern machine learning infrastructure.
- Ability to work effectively with technologies such as AWS, SLURM, Docker, CI/CD pipelines, and distributed training and inference systems.
- Research-driven mindset combined with a strong focus on practical outcomes and shipping production solutions.
- Ability to explore multiple approaches quickly, identify promising directions, and discontinue low-value experiments when appropriate.
- Strong scientific communication skills, with the ability to clearly present experimental results and technical conclusions.
- High degree of autonomy, ownership, adaptability, and initiative, combined with a collaborative approach to working across teams.
Benefits
- Fully remote working environment within Europe.
- Full-time employment.
- Opportunity to build and work on production-scale video foundation models at the forefront of Generative AI.
- Direct opportunity to influence next-generation human-centric video generation technology.
- Work on challenging technical problems involving scalability, model stability, controllability, evaluation, and inference optimization.
- High-ownership environment where research and engineering contributions are designed to reach real-world products.
- Opportunity to collaborate with highly technical AI researchers and engineers.
- Exposure to large-scale machine learning infrastructure, distributed computing, and production AI systems.
- Opportunity to work on technology serving tens of thousands of businesses worldwide.
- Fast-paced environment that encourages autonomy, experimentation, scientific thinking, and measurable impact.
- Opportunity to contribute to AI technology with a strong focus on safety, ethics, security, and people-first development.


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