At a Glance
- Tasks: Own and optimise ML infrastructure for large-scale visual data processing.
- Company: Dynamic AI tech company transforming the media industry with machine learning.
- 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 productionising cutting-edge ML models in a fast-paced environment.
- Qualifications: Strong ML engineering background with Python and experience in production systems.
The predicted salary is between 60000 - 80000 £ 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, life cycle 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/Front End for internal tooling
Senior Machine Learning Systems Engineer - Visual Data, Vision ML in London employer: Staffworx Limited
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 Systems Engineer, you will thrive in a collaborative work culture that prioritises employee growth and development, offering opportunities to take ownership of core ML infrastructure and make a tangible impact on production systems. With a focus on building reliable platforms and optimising performance, this role provides a unique chance to work with large-scale multimodal datasets in a fast-paced environment, all while enjoying the benefits of a privately backed firm that values creativity and outcome-driven results.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Systems Engineer - Visual Data, Vision ML in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML infrastructure and productionisation. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on practical scenarios. Be ready to discuss how you've tackled challenges in ML systems and how you’ve optimised pipelines. Real-world examples will make you stand out!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Senior Machine Learning Systems Engineer - Visual Data, Vision ML in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your ML engineering background, especially in productionisation and infrastructure, to show us you're the right fit for the role.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about transitioning from research to platform ownership. Share specific examples of how you've made ML systems fast and scalable in previous roles to grab our attention.
Showcase Relevant Projects:Include any projects or experiences that demonstrate your hands-on skills with PyTorch, distributed systems, and large-scale data pipelines. We love seeing practical applications of your knowledge!
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’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Staffworx Limited
✨Know Your ML Infrastructure Inside Out
Make sure you can discuss your experience with ML infrastructure in detail. Be prepared to explain how you've built and optimised data platforms, and share specific examples of how you've improved training performance or managed model lifecycles.
✨Showcase Your Python and Production Skills
Since strong Python skills are a must, brush up on your coding abilities. Be ready to demonstrate your knowledge of production languages like C++ or Java, and discuss how you've applied these in real-world scenarios, especially in relation to data pipelines and performance trade-offs.
✨Bridge the Gap Between Experimentation and Production
Highlight your ability to transition from research to production. Prepare to talk about how you've taken models from experimentation to deployment, focusing on the challenges you faced and how you overcame them to ensure reliability and scalability.
✨Familiarise Yourself with Relevant Tools and Technologies
Get comfortable with tools mentioned in the job description, like PyTorch, Ray, and Triton. If you have experience with video processing or vector search systems, be sure to bring that up. Showing familiarity with these technologies will demonstrate your readiness for the role.
