At a Glance
- Tasks: Join our MLOps team to deploy and optimise machine learning models in production.
- Company: Dynamic tech company focused on AI-driven solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Exciting environment with strong focus on collaboration and continuous improvement.
- Why this job: Make a real impact by collaborating on innovative AI projects with cutting-edge technology.
- Qualifications: Experience in machine learning, Python, and cloud platforms; growth mindset is key.
The predicted salary is between 60000 - 80000 £ per year.
Requirements
- Solid technical foundation in both machine learning and software engineering.
- Experience deploying machine learning models in production environments in cloud platforms like GCP, AWS or Azure.
- Experience with CI/CD pipelines for machine learning (e.g., Github action, Docker).
- Experience with ML platforms/frameworks (e.g., VertexAI, Kubeflow, Sagemaker).
- Experience with data processing frameworks and tools (e.g., Spark, Databricks), particularly Apache Beam/Dataflow is highly desirable.
- Knowledge of monitoring and maintaining models in production.
- Experience with performance/cost optimization is highly desirable (e.g., Latency, throughput).
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Problem-solving skills with the ability to troubleshoot model and pipeline issues.
- Strong communication skills, enabling effective collaboration across teams.
What the job involves
- As part of the MLOps team, you’ll work closely with data scientists, software engineers, and other stakeholders to bring machine learning models to life—ensuring they’re deployed, maintained, and monitored efficiently in production.
- Collaborate with data scientists to take machine learning models from development to production, ensuring high performance and scalability.
- Develop and maintain data and model pipelines, integrating seamlessly with our existing systems to support reliable, efficient workflows.
- Design and implement continuous integration and delivery pipelines to streamline the deployment of machine learning models.
- Help monitor the performance of machine learning models post-deployment, ensuring reliability, scalability, and quality over time.
- Work with cross-functional teams to design solutions that meet business needs while adhering to best practices in machine learning and software engineering.
- Continuously improve our infrastructure, ensuring we remain at the forefront of AI model production and delivery.
- Develop MCP servers and A2A agents through our internal framework for managing multi-agent orchestrated deployments.
Machine Learning Engineer employer: Trustpilot
At Trustpilot, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Machine Learning Engineer, you'll have access to cutting-edge technology and the opportunity for continuous professional growth, all while working in a supportive environment that values your contributions. Our commitment to employee development, coupled with our focus on AI-driven solutions, makes Trustpilot a rewarding place to advance your career in the heart of a vibrant tech community.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. 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 machine learning projects. Whether it's a GitHub repo or a personal website, let your work speak for itself.
✨Tip Number 3
Prepare for those interviews! Brush up on common ML concepts and be ready to discuss your past experiences. Practising with mock interviews can really boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications from passionate candidates who are eager to grow and collaborate with us in the MLOps team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your technical skills in machine learning and software engineering. We want to see your experience with cloud platforms and CI/CD pipelines, so don’t hold back on showcasing your expertise!
Emphasise Your Growth Mindset:Even if you don’t tick every box in the job description, let us know about your willingness to learn and adapt. We value a growth mindset, so share examples of how you've tackled challenges and learned new skills.
Tailor Your Application:Take the time to customise your application for this role. Use keywords from the job description and relate your experiences to what we’re looking for. This shows us that you’re genuinely interested in joining our team!
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’s super easy to do!
How to prepare for a job interview at Trustpilot
✨Know Your Tech Inside Out
Make sure you brush up on your machine learning and software engineering fundamentals. Be ready to discuss your experience with cloud platforms like GCP, AWS, or Azure, and be prepared to dive into specifics about CI/CD pipelines and ML frameworks you've worked with.
✨Show Off Your Problem-Solving Skills
Prepare to share examples of how you've tackled challenges in deploying machine learning models. Think about times when you had to troubleshoot issues or optimise performance—these stories will showcase your problem-solving abilities and technical expertise.
✨Collaboration is Key
Since this role involves working closely with data scientists and software engineers, be ready to discuss how you've collaborated in the past. Highlight your communication skills and any experiences where teamwork led to successful project outcomes.
✨Stay Curious and Open-Minded
Emphasise your growth mindset during the interview. Talk about how you approach learning new technologies or frameworks, and express your enthusiasm for continuous improvement. Companies value candidates who are eager to learn and adapt!
