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 fast-paced environment with exciting challenges and career advancement.
- Why this job: Make a real impact by bridging research and production in machine learning.
- Qualifications: Experience in ML engineering and a passion for building scalable systems.
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 performance for deployed models
Senior Machine Learning Engineer employer: Staffworx
As a Senior Machine Learning Engineer at our innovative AI technology company, you will thrive in a dynamic work culture that prioritises collaboration and creativity. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages growth and exploration in the rapidly evolving field of machine learning. Located at the heart of the media industry, our team is dedicated to transforming cutting-edge technology into impactful solutions, making this an exciting place for those looking to make a meaningful contribution.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML infrastructure and production systems. We want to see how you’ve tackled real-world problems and made an impact.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. We recommend doing mock interviews with friends or using platforms that simulate the interview experience. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Machine Learning Engineer
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 experience with ML infrastructure, data platforms, and any relevant projects that showcase your ability to bridge experimentation and production.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about machine learning in the media industry. Share specific examples of how you've transitioned from research to platform ownership and how you’ve made ML systems fast and scalable in previous roles.
Showcase Your Projects:If you've worked on any projects involving large-scale multimodal datasets or ML pipelines, make sure to include them in your application. We love seeing real-world applications of your skills, so don’t hold back!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Staffworx
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like PyTorch and Ray. Brush up on your knowledge of ML pipelines and data platforms, as you’ll want to demonstrate your ability to build and optimise these systems during the interview.
✨Showcase Your Production Experience
Since this role focuses on productionising models, be ready to discuss your past experiences where you’ve taken models from experimentation to production. Share specific examples of challenges you faced and how you overcame them, highlighting your product-minded approach.
✨Prepare for Technical Questions
Expect technical questions that test your understanding of distributed training and large-scale datasets. Practice explaining complex concepts clearly and concisely, as you may need to bridge the gap between technical details and practical applications during the interview.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s current ML infrastructure and future projects. This shows your genuine interest in the role and helps you assess if the company aligns with your career goals, especially regarding platform ownership and scalability.
