Senior ML Systems Engineer

Flawless Holdings; Greater London; Hybrid; GBP 57,000 - 97,000; Full time

Benefits offered by this job

Hybrid work

Autonomy

Stock options

Competitive salary

Job summary

Flawless, London-based AI company transforming Hollywood with assistive AI, is hiring an ML Systems Engineer from Senior to Staff level to build scalable data platforms and ML infrastructure.

You will design and optimize data pipelines, model training and inference infrastructure, and collaborate with scientists and platform teams to deliver production-grade systems. Strong Python, PyTorch, and distributed systems experience is required; hybrid work with generous stock options is offered.

Qualifications

• Experience building ML infrastructure, ML platforms, or large-scale backend systems.
• Strong Python engineering skills and production services experience.
• Deep understanding of data pipelines and performance trade-offs.
• Hands-on experience with PyTorch and distributed systems.
• Experience working with large-scale datasets and high-throughput pipelines.
• Familiarity with data storage and analytics tech, including data lakes and columnar formats.
• Strong debugging, problem-solving, and systems design skills.
• Ability to collaborate with cross-functional teams.
• Staff level: technical leadership across infrastructure initiatives, architecture and strategy.

Responsibilities

• Build and evolve data platforms to curate and manage large-scale multimodal datasets.
• Design systems to index, process, and enrich thousands of videos via ML pipelines.
• Optimize storage and access for model training and experimentation.
• Improve reliability, scalability, and observability across the data ecosystem.
• Build and optimize infrastructure for large-scale model training.
• Enhance performance in single-node and distributed training environments.
• Scale data loading, preprocessing, and training workflows.
• Ensure training pipelines are reproducible, efficient, and operable.
• Develop systems for collecting, storing, and analysing model outputs.
• Create tooling for dataset exploration, experiment tracking, and model comparison.
• Enable scientists to iterate rapidly with robust evaluation practices.
• Design and maintain infrastructure for model versioning, experimentation, validation, and deployment.
• Improve reproducibility and governance across the ML lifecycle.
• Support promotion of models from research to production.
• Build and optimize inference infrastructure for production workloads.
• Define and improve model serving protocols and deployment patterns.
• Enhance performance, reliability, and scalability of production inference systems.
• Collaborate with scientists, ML engineers, and platform teams on system design.
• Senior staff will provide technical leadership and drive architectural decisions.

Skills

ML Systems

Python

Data pipelines

Distributed systems

PyTorch

Model training

Model serving

Video / multimodal ML

Frontend basics (React)

Tools

PyTorch

React

NodeJS

Job description

Salary: £57,000 - 97,000 per year

Requirements

• We are looking for experienced ML Systems Engineers, open from Senior Engineer through Staff Engineer levels.
• We value experience building machine learning infrastructure, ML platforms, data platforms, or large-scale backend systems.
• We want strong Python engineering skills and experience building production services.
• We look for a deep understanding of data pipelines and performance trade-offs across storage, networking, memory, and compute.
• We expect hands‑on experience with machine learning frameworks such as PyTorch.
• We value experience building and operating distributed systems.
• We look for experience working with large-scale datasets and high-throughput data processing pipelines.
• We prefer familiarity with modern data storage and analytics technologies, including columnar data formats and data lake architectures.
• We need strong debugging, problem-solving, and systems design skills.
• We value effective collaboration with cross‑functional teams.
• For Staff Engineers, we expect technical leadership across significant infrastructure initiatives, architecture and technical strategy experience, influence beyond an individual team, mentoring ability, and a track record of balancing immediate research needs with long‑term platform investments.
• Nice to have: experience with video, media, or multimodal machine learning pipelines; embeddings, vector search, or retrieval systems; production inference systems; and frontend experience with React or similar for internal tools and workflows.

Responsibilities

• We will have you build and evolve data platforms used to curate and manage large‑scale multimodal datasets.
• We will have you design systems that index, process, and enrich thousands of videos through machine learning pipelines.
• We will have you optimize data storage and access patterns for efficient model training and experimentation.
• We will have you improve reliability, scalability, and observability across the data ecosystem.
• We will have you build and optimize infrastructure for large‑scale model training.
• We will have you improve performance across single‑node and distributed training environments.
• We will have you scale data loading, preprocessing, and training workflows.
• We will have you ensure training pipelines are reproducible, efficient, and easy to operate.
• We will have you develop systems for collecting, storing, and analyzing model outputs.
• We will have you build tooling for dataset exploration, experiment tracking, and model comparison.
• We will have you enable scientists to iterate rapidly while maintaining robust evaluation practices.
• We will have you design and maintain infrastructure for model versioning, experimentation, validation, and deployment.
• We will have you improve reproducibility and governance across the machine learning lifecycle.
• We will have you support the promotion of models from research through production.
• We will have you build and optimize inference infrastructure for production workloads.
• We will have you define and improve model serving protocols and deployment patterns.
• We will have you enhance performance, reliability, and scalability of production inference systems.
• We will have you work closely with scientists, machine learning engineers, and platform teams to design and build the systems that underpin model development and deployment.
• Senior candidates will provide technical leadership, drive architectural decisions, mentor other engineers, and influence infrastructure strategy across multiple initiatives.

Technologies

• AI
• Backend
• Frontend
• Support
• Machine Learning
• Model Serving
• Model Training
• PyTorch
• Python
• React
• NodeJS

More

We are Flawless, an AI company transforming Hollywood with assistive AI that helps filmmakers edit, localize, and refine performances while preserving artistic intent. Our technology is designed to support artists, not replace them, and helps enable seamless multilingual releases, avoid costly reshoots, and expand creative reach. We are also setting the standard for ethical AI in entertainment through our Artistic Rights Treasury (A.R.T.), a rights management solution that protects artists and rights holders with transparency and respect for creative ownership. This role sits in our Research Services team within Technology, working at the intersection of large-scale data systems, machine learning, and high‑performance computing. London‑based team values trust, collaboration, and a caring, creative culture.

Benefits

• hybrid working environment
• autonomy
• competitive salary
• generous stock options for all permanent employees

last updated 37 week of 2026