Jobs in Newcastle, United Kingdom

We’re looking for a Machine Learning Engineer to design and scale the infrastructure powering our generative video and visual models, particularly for our latest product verv.fm . This role combines deep ML engineering expertise with hands-on experience in modern generative systems like Stable Diffusion, Comfy, and Flux.

You’ll build data ingestion pipelines, develop visual preprocessing workflows, and fine-tune generative models with production-level reliability. You’ll also help drive prompt-aware systems and intelligent feedback loops that elevate both the quality and control of our media generation stack.

Design and implement data workflows for ingestion, cleaning, validation, filtering, and quality scoring

Fine-tune and deploy generative models like Stable Diffusion and Flux Loras and build large-scale workflows using tools such as Comfy

Experience training and working with video models like WAN, VACE, etc

Integrate computer vision techniques (segmentation, mask ops, object tracking) into generation pipelines

Build high-throughput pipelines for frame extraction, captioning, and visual data processing at scale

Develop and experiment with context- and prompt-aware model orchestration strategies

Contribute to observability and monitoring across the ML data lifecycle

Collaborate with infra teams to scale across GPU-backed infrastructure and serverless environments (e.g., Fal.ai )

Work across a fast-paced, evolving product environment with creative and technical inputs

(Bonus) Explore self-supervising or agentic workflows for automated pipeline feedback and improvement

Strong experience in ML engineering with a focus on computer vision, generative media, or multimodal systems

Hands-on experience fine-tuning or deploying generative models (e.g., SD, Flux, Comfy)

Proficiency in Python and asynchronous API development

Familiarity with image/video-specific challenges: frame alignment, codec handling, perceptual quality scoring

Experience with scalable data systems using tools like Airflow, Spark, or Ray

Solid understanding of GPU infrastructure and model deployment best practices

Knowledge of prompt engineering and context-driven model behavior

Comfortable working in ambiguity and bridging infrastructure and modeling challenges

Bonus: Experience with foundation model training pipelines or agentic systems

Availability for at least 4 hours of overlap with US Eastern Time

Competitive salary and equity

Private health coverage

Unlimited paid vacation

Fully-distributed, async-first culture

Hardware setup of your choice

Stipends for phone, internet, and meals

Moonvalley is an equal opportunity employer and we are committed to providing accommodations. If you require accommodation, we will work with you to meet your needs.

Please be assured we'll treat any information you share with us with the utmost care, only use your information for recruitment purposes and will never sell it to other companies for marketing purposes.