At a Glance
- Tasks: Design, build, and operate mission-critical machine learning capabilities with innovative teams.
- Company: Join MI6 and be part of a cutting-edge AI initiative.
- Benefits: Competitive salary, career development, and the chance to work on impactful projects.
- Other info: Collaborative environment with opportunities to mentor and lead innovative projects.
- Why this job: Make a real difference by transforming theoretical ideas into robust systems.
- Qualifications: Experience in ML lifecycle, Python, and familiarity with MLOps tools.
The predicted salary is between 55000 - 65000 € per year.
There's no single route into this role – what matters most is your genuine, end-to-end understanding of the ML lifecycle and real, practical experience of model deployment, monitoring, and optimisation. This may come from working as a Machine Learning Engineer already, years spent as a Software Engineer building applied ML systems, or a data science background grounded in strong mathematical foundations.
You’ll have seen at least one full project lifecycle through from start to finish, ideally within an environment with centralised standards and platforms. With your background of managing model lifecycles and deployments, you’ll know what it takes to fine-tune and operationalise models in live environments, and you’ll be comfortable working across platforms with the ability to adapt to customer-embedded models as needs evolve.
Hands-on experience working alongside scientists and within deployment environments is central to this role, as is familiarity with MLOps tooling, scheduling, and orchestration. You’ll be comfortable working in cloud environments and with ML lifecycle tools such as Weights & Biases, and you’ll have Python experience too. Experience with Docker or Kubernetes is beneficial, but not essential.
You’re also curious, self-directed, and always keen to learn more. You’re comfortable with ambiguity, calm under pressure, and able to see beyond the immediate task. You communicate clearly, work well within technical communities, and bring the resilience to keep things moving should any obstacles arise.
Enhancing how we operate today will better prepare us for the challenges of tomorrow. That’s why we’re creating dedicated teams focused on bringing Artificial Intelligence (AI) into some of our most mission-critical areas. As a Machine Learning Engineer, you’ll be at the heart of our efforts, working with our technical teams to design, build and operate mission-critical machine learning capabilities. This includes everything from infrastructure and MLOps through to training, fine-tuning, deployment, and live model management.
While you’ll be hands-on in engineering, your work will also be advisory, ensuring models are reliable, scalable, and fit for use. Day to day, you’ll collaborate with multidisciplinary teams of data scientists, researchers, and hosting specialists, acting as the subject-matter expert for ML deployment, reliability, and best practice. You’ll be the critical bridge between research and production, making sure ideas don’t stay theoretical but instead become robust, impactful systems.
A large part of your role will be leading on the technical evaluation of environments and workflows, driving innovation-led initiatives, and continuously improving how we monitor, measure, and optimise model performance. Just as importantly, you’ll champion model explainability, transparency, and ethical AI as core engineering principles. Across multiple projects, you’ll also support and mentor junior ML engineers, help software engineers design ML-ready hosting environments, and translate complex systems into a clear, practical understanding for non-technical stakeholders.
Machine Learning Engineer (MI6) employer: Deepstreamtech
At MI6, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As a Machine Learning Engineer, you'll have the opportunity to work on mission-critical projects that directly impact national security, all while benefiting from a supportive environment that encourages continuous learning and professional growth. With access to cutting-edge technology and a commitment to ethical AI practices, you'll be part of a team that values your expertise and contributions, making your work both meaningful and rewarding.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (MI6)
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the machine learning community. Attend meetups, webinars, or even online forums. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those that highlight your experience with model deployment and optimisation. Share it on platforms like GitHub or your personal website, and don’t forget to link it in your applications!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical stakeholders. Mock interviews can be a game changer!
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your hands-on experience with MLOps tools and cloud environments, and let us see how you can contribute to our mission at MI6.
We think you need these skills to ace Machine Learning Engineer (MI6)
Some tips for your application 🫡
Show Your ML Journey:When you’re writing your application, make sure to highlight your journey through the machine learning lifecycle. We want to see your hands-on experience with model deployment and optimisation, so don’t hold back on sharing those real-world examples!
Be Clear and Concise:We love a good story, but keep it relevant! Use clear language to describe your experiences and skills. Remember, we’re looking for someone who can communicate effectively, so make sure your application reflects that.
Tailor Your Application:Don’t just send a generic application our way. Take the time to tailor your CV and cover letter to the role. Mention specific tools and technologies from the job description, like MLOps tooling or Python, to show us you’re the perfect fit!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our team at StudySmarter!
How to prepare for a job interview at Deepstreamtech
✨Know Your ML Lifecycle
Make sure you can confidently discuss the entire machine learning lifecycle. Be prepared to share specific examples from your past projects where you managed model deployment, monitoring, and optimisation. This will show that you have the hands-on experience they’re looking for.
✨Showcase Your Technical Skills
Brush up on your Python skills and be ready to talk about any MLOps tools you've used, like Weights & Biases. If you have experience with Docker or Kubernetes, even better! Highlighting your technical expertise will demonstrate that you can hit the ground running.
✨Communicate Clearly
Since you'll be working with multidisciplinary teams, practice explaining complex concepts in simple terms. Think about how you would describe your work to someone without a technical background. Clear communication is key to bridging the gap between research and production.
✨Emphasise Your Curiosity and Adaptability
They want someone who is self-directed and eager to learn. Be ready to discuss how you've tackled ambiguity in past roles and how you stay calm under pressure. Sharing examples of how you've adapted to changing needs will show that you're the right fit for their dynamic environment.
