Senior ML Engineer: Architect Production ML Pipelines

Senior ML Engineer: Architect Production ML Pipelines

Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
Sage

At a Glance

  • Tasks: Lead the development of scalable machine learning pipelines and manage the entire ML lifecycle.
  • Company: Sage, a leading tech company in Newcastle upon Tyne with a focus on innovation.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Exciting opportunity to shape the future of ML in a collaborative environment.
  • Why this job: Join a dynamic team and make a significant impact in the world of machine learning.
  • Qualifications: Experience in machine learning and software engineering best practices.

The predicted salary is between 60000 - 80000 £ per year.

Sage is seeking a Senior ML Engineer in Newcastle upon Tyne to lead the technical ownership of their machine learning production environment. This hybrid role requires the individual to transition experimental models into reliable, scalable services, ensuring fiscal responsibility in deploying these models.

The ideal candidate will manage the entire ML lifecycle and will be involved in establishing best practices for software engineering within the team.

Senior ML Engineer: Architect Production ML Pipelines employer: Sage

Sage is an exceptional employer that fosters a collaborative and innovative work culture, particularly in the vibrant city of Newcastle upon Tyne. Employees benefit from a strong focus on professional development, with ample opportunities for growth and learning in the rapidly evolving field of machine learning. The company also prioritises work-life balance and offers a hybrid working model, making it an attractive choice for those seeking meaningful and rewarding employment.

Sage

Contact Details:

Sage Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior ML Engineer: Architect Production ML Pipelines

Join Local Tech Meetups

Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Sage or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!

Contribute to Open Source Projects

Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Sage.

Tap into Online Developer Communities

Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Sage.

Explore Job Boards Specifically for Tech Roles

Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Sage that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!

We think you need these skills to ace Senior ML Engineer: Architect Production ML Pipelines

Machine Learning
Model Deployment
Scalability
Software Engineering Best Practices
Technical Ownership
ML Lifecycle Management
Fiscal Responsibility

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Sage.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Sage and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Sage

Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Sage uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.