flawless - Senior / Staff / Principal ML Systems Engineer

London+ Equity2w ago

In Office Principal EMEA Artificial Intelligence Systems Engineer Principal Python React Vector Mentoring Governance

Requirements

• We're interested in engineers who enjoy building systems that make machine learning teams more effective and productive. • WE'RE PARTICULARLY INTERESTED IN CANDIDATES WITH: • Experience building machine learning infrastructure, ML platforms, data platforms, or large-scale backend systems. • Strong Python engineering skills and experience building production services. • Deep understanding of data pipelines and performance trade-offs across storage, networking, memory, and compute. • Hands-on experience working with machine learning frameworks such as PyTorch. • Experience building and operating distributed systems. • Experience working with large-scale datasets and high-throughput data processing pipelines. • Familiarity with modern data storage and analytics technologies, including columnar data formats and data lake architectures. • Strong debugging, problem-solving, and systems design skills. • Experience collaborating effectively with cross-functional teams. • ADDITIONAL EXPECTATIONS FOR STAFF ENGINEERS • Demonstrated technical leadership across significant infrastructure initiatives. • Experience defining architecture and technical strategy for complex systems. • Ability to influence engineering direction beyond an individual team. • Track record of mentoring engineers and raising technical standards. • Experience balancing immediate research needs with long-term platform investments. • Experience working with video, media, or multimodal machine learning pipelines. • Familiarity with embeddings, vector search, or retrieval systems. • Experience operating production inference systems. • Frontend experience (React or similar) for building internal tools and workflows.

Responsibilities

• DATA PLATFORMS FOR MACHINE LEARNING • Build and evolve data platforms used to curate and manage large-scale multimodal datasets. • Design systems that index, process, and enrich thousands of videos through machine learning pipelines. • Optimize data storage and access patterns for efficient model training and experimentation. • Improve reliability, scalability, and observability across the data ecosystem. • ML TRAINING INFRASTRUCTURE • Build and optimize infrastructure for large-scale model training. • Improve performance across single-node and distributed training environments. • Scale data loading, preprocessing, and training workflows. • Ensure training pipelines are reproducible, efficient, and easy to operate. • EVALUATION & EXPERIMENTATION SYSTEMS • Develop systems for collecting, storing, and analyzing model outputs. • Build tooling for dataset exploration, experiment tracking, and model comparison. • Enable scientists to iterate rapidly while maintaining robust evaluation practices. • MODEL LIFECYCLE MANAGEMENT • Design and maintain infrastructure for model versioning, experimentation, validation, and deployment. • Improve reproducibility and governance across the machine learning lifecycle. • Support the promotion of models from research through production. • PRODUCTION INFERENCE SYSTEMS • 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.

Benefits

• You will be working in an environment based on trust, autonomy and collaboration, and this is a great opportunity for someone who wants to be part of a growing company in its most exciting stage of development. You can play a part in shaping the future of a company that’s caring, creative and collaborative. • In addition to this, you'll also receive: • A hybrid working environment • All permanent employees receive generous stock options • I don’t meet all the listed requirements—should I still apply? • Absolutely! Research shows that women and underrepresented groups often hesitate to apply unless they meet every qualification, but at Flawless, we actively work to break down those barriers. We believe diverse perspectives, experiences, and backgrounds make us stronger, and we are committed to supporting and elevating underrepresented talent. If you're excited about the role, share our values, and believe you can contribute meaningfully, we encourage you to apply—even if you don’t meet every single requirement. Your unique skills and perspective matter, and we’d love to hear from you ❤️