At a Glance
- Tasks: Own and optimise ML infrastructure for large-scale video data processing.
- Company: Dynamic AI tech company transforming the media industry.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join a collaborative team focused on innovation and scalability.
- Why this job: Make a real impact by bridging experimentation and production in cutting-edge ML projects.
- Qualifications: Strong ML engineering background with Python and experience in production environments.
The predicted salary is between 70000 - 90000 £ per year.
A privately backed AI technology company operating at the intersection of machine learning in the media industry. You will own core ML infrastructure end to end, from data ingestion and curation through to distributed training and production inference, working with large-scale multimodal datasets (video, embeddings, metadata).
This is not a research role. The focus is on productionising models, building reliable platforms, and making ML systems fast and scalable in a real production environment. The ideal profile is an ML engineer transitioning from research into platform ownership - someone who is product-minded and outcome-driven rather than tech-for-tech's-sake. You should be comfortable bridging the gap between experimentation and production.
Key Responsibilities- Build and evolve a data platform (LanceDB, DataFusion, SQL and vector search) for large-scale multimodal datasets
- Design ML pipelines for video indexing and processing (face detection, quality assessment, tracking)
- Improve training performance across single and multi-node setups using PyTorch and Ray
- Build evaluation and experimentation systems (Parquet/Iceberg) for model output analysis
- Own model versioning, lifecycle management, and promotion to production
- Optimise inference pipelines using Triton; build model ensembles and define request protocols
- Proven ML engineering background with a focus on infrastructure and productionisation (not just model training)
- Strong Python skills, plus experience with a robust production language such as C++ or Java
- Solid understanding of data pipeline performance trade-offs: I/O, compute, batching, memory layout
- Hands-on PyTorch experience: training pipelines, data loading, preprocessing
- Practical distributed systems experience (Ray, DDP, or similar)
- Experience handling TB-scale or high-throughput data pipelines
- Familiarity with columnar formats: Arrow, Parquet, Iceberg
- Exposure to video or visual media pipelines (FFmpeg, encoding, frame extraction)
- Vector search or embedding system experience
- Triton or production inference background
- React/frontend for internal tooling
Senior Machine Learning Engineer in London employer: Staffworx
Join a dynamic and innovative AI technology company that is at the forefront of machine learning in the media industry. As a Senior Machine Learning Engineer, you will thrive in a collaborative work culture that values creativity and practical problem-solving, with ample opportunities for professional growth and development. Enjoy the unique advantage of working in a fast-paced environment where your contributions directly impact the production of cutting-edge ML systems, all while being part of a supportive team dedicated to excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML infrastructure and productionisation. We want to see how you’ve tackled real-world problems, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on practical scenarios. Think about how you’d optimise ML pipelines or handle large-scale datasets. We’re looking for problem solvers, so be ready to demonstrate your thought process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take that extra step to connect with us directly.
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Senior Machine Learning Engineer role. Highlight your ML engineering background, especially in productionisation and infrastructure, to show us you’re the right fit.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about bridging the gap between experimentation and production. Share specific examples of how you've built reliable platforms or optimised ML systems in the past.
Showcase Your Projects:If you've worked on relevant projects, especially those involving large-scale multimodal datasets or video processing, make sure to include them. We love seeing practical applications of your skills!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team.
How to prepare for a job interview at Staffworx
✨Know Your ML Infrastructure Inside Out
Make sure you can discuss your experience with building and evolving data platforms. Be ready to explain how you've handled large-scale multimodal datasets and the specific tools you've used, like LanceDB or DataFusion. This will show that you're not just familiar with the tech but have practical experience in productionising models.
✨Showcase Your Python and Distributed Systems Skills
Prepare to talk about your strong Python skills and any experience you have with languages like C++ or Java. Highlight your hands-on experience with distributed systems, especially using frameworks like Ray. Being able to articulate how you've optimised training performance will set you apart.
✨Bridge the Gap Between Experimentation and Production
Since this role focuses on production rather than research, be ready to discuss how you've transitioned models from experimentation to a production environment. Share specific examples of how you've improved inference pipelines or managed model versioning and lifecycle management.
✨Familiarity with Video and Visual Media Pipelines
If you have experience with video processing tools like FFmpeg or have worked with vector search systems, make sure to mention it. This could give you an edge, as familiarity with these areas aligns well with the job's requirements. Prepare to discuss any relevant projects or challenges you've tackled in this space.
