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- Tasks: Own and enhance ML pipelines, collaborating with scientists to drive improvements.
- Company: Fast-growing tech company focused on data-driven solutions and machine learning.
- Benefits: Competitive rate of £600/day, hybrid or remote work options.
- Other info: Inclusive culture with opportunities for professional growth.
- Why this job: Join a dynamic team and make a real impact on user experience through ML.
- Qualifications: Strong Python skills and experience with ML pipelines and AWS.
The predicted salary is between 120000 - 120000 £ per year.
Job type: Contract
Contract Length: 6 months
Rate: £600/day+
Role Location: Hybrid or remote (Farringdon, London)
The company: A fast-growing, product-focused technology company operating a large-scale, data-driven platform. The business places a strong emphasis on machine learning to enhance user experience and platform safety, with a collaborative, cross-functional engineering culture.
Role and Responsibilities:
- Own and improve ML retraining pipelines to reduce manual effort for ML scientists.
- Enhance model deployment and inference pipelines (primarily using AWS SageMaker).
- Improve observability, monitoring, and overall performance of ML systems.
- Work closely with ML scientists to identify pain points and translate them into scalable solutions.
- Optimise asynchronous inference pipelines (Kafka, RabbitMQ).
- Implement features such as shadow deployments, A/B testing, and enhanced metrics.
- Improve CI/CD pipelines to accelerate model iteration and deployment.
- Collaborate within a cross-functional product squad.
Job Requirements:
- Strong Python engineering skills.
- Experience with ML training and deployment pipelines.
- Hands-on experience with AWS (ideally SageMaker).
- Experience with Docker and containerisation.
- Solid understanding of CI/CD processes.
- Experience with Kafka or similar asynchronous systems (e.g. RabbitMQ).
- Ability to work independently and drive engineering improvements.
- Experience with LLMs, text-based models, or detection systems is a plus.
Accessibility Statement: We make an active choice to be inclusive towards everyone every day. Please let us know if you require any accessibility adjustments through the application or interview process.
Machine Learning Engineer employer: Signify Technology
Contact Detail:
Signify Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech world, especially those in machine learning. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML pipelines or AWS. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and tools. Be ready to discuss your experience with Python, Docker, and CI/CD processes. Confidence in your knowledge will impress interviewers!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python engineering skills and experience with ML training and deployment pipelines. We want to see how your background aligns with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the Machine Learning Engineer position and how your experience with AWS SageMaker and CI/CD processes makes you a perfect fit for our team.
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled challenges in previous roles, especially around optimising ML systems or improving pipelines. We love seeing candidates who can think critically and drive engineering improvements!
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 during the process!
How to prepare for a job interview at Signify Technology
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, AWS SageMaker, and Docker. Brush up on your knowledge of ML pipelines and CI/CD processes, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, particularly around optimising ML systems or improving deployment pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your impact.
✨Collaborative Mindset
Since the role involves working closely with ML scientists and cross-functional teams, be ready to demonstrate your teamwork skills. Share examples of how you’ve successfully collaborated on projects and how you handle feedback and differing opinions.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the company’s goals and culture. Inquire about their current ML projects, the team dynamics, or how they measure success in their ML initiatives. This shows you’re genuinely interested in contributing to their mission.