At a Glance
- Tasks: Lead the development and deployment of machine learning systems in real-world applications.
- Company: Fast-growing AI consultancy focused on delivering intelligent systems.
- Benefits: Competitive salary, remote-first work, and opportunities for professional growth.
- Other info: Join a dynamic team that values practical solutions over theoretical concepts.
- Why this job: Make a real impact by applying ML in complex environments and working directly with clients.
- Qualifications: 6-8+ years in data science, strong statistics background, and experience in production environments.
The predicted salary is between 100000 - 170000 € per year.
If you’re a data scientist who actually enjoys getting things into production — not just building models — this is worth a look. Immersum are working with a fast-growing AI consultancy delivering intelligent systems into complex, regulated environments. They’re hiring Principal engineers who can own problems end-to-end: from shaping the approach, through modelling, to deploying something that genuinely works in the real world. This isn’t a research role. It’s applied, hands-on, and client-facing.
You’ll be working directly with stakeholders, building and deploying ML systems using Python and modern cloud environments (AWS / Azure), and helping organisations understand whether what they’ve built is actually performing — and why. The sweet spot is someone who’s data science-led but engineering-capable. You’ll spend your time applying statistical modelling and classical machine learning techniques, designing experiments that tie back to commercial outcomes, and ensuring what gets built can actually scale in production systems.
There’s an expectation you can take something from idea → prototype → production without handing it off. You’ll also be in front of clients. Not in a salesy way — more in a “this is what the data says, here’s what’s working, here’s what isn’t” kind of way. Being able to explain performance, handle challenge, and build trust matters. There is also increasing exposure to AI and LLM-driven use cases, but this isn’t an LLM-only role — strong fundamentals in ML and statistics are far more important than prompt engineering alone.
They look for:
- ~6-8+ years in data science / machine learning
- Strong grounding in statistics, experimentation, and evaluation methods
- Experience deploying ML systems into production environments
- Solid engineering capability (typically Python-based stacks)
- Exposure to cloud platforms (AWS or Azure)
You’ll stand out if you’ve worked in more complex or regulated environments — particularly across pharma, biopharma, medtech, or manufacturing — and understand what “production-ready” really means in those settings.
Additional things that tend to separate strong candidates:
- Experience working with ML in regulated environments (e.g. GAMP frameworks)
- Familiarity with regulatory standards like ISO 27001, ISO 13485, or GxP (GDP / GMP)
- Practical experience applying LLMs across real business use cases (not just experimentation)
- Exposure to manufacturing or biopharma data science problems
Where people tend to do well here:
- You’re pragmatic. You care more about impact than elegance.
- You’re comfortable owning a problem properly, not just your slice of it.
- And you don’t mind getting pulled into conversations with stakeholders when needed.
Where it’s probably not a fit:
- If you prefer pure research, or you’ve never taken a model into production, or you want to stay completely removed from clients — this will feel uncomfortable.
In return, you get exposure to genuinely interesting problems, end-to-end ownership, and a team that values delivery over theory.
Principal Machine Learning Engineer employer: Immersum
Immersum is an exceptional employer for Principal Machine Learning Engineers, offering a remote-first work culture that prioritises flexibility while maintaining client engagement. With a focus on applied machine learning in complex environments, employees benefit from hands-on experience, opportunities for professional growth, and the chance to tackle meaningful challenges that have real-world impact. The company fosters a collaborative atmosphere where innovation thrives, making it an ideal place for those looking to advance their careers in AI and ML.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. The more people you know, the better your chances of landing that Principal ML Engineer role.
✨Tip Number 2
Showcase your hands-on experience! When chatting with potential employers, highlight specific projects where you've taken models from idea to production. They want to see your practical skills in action, so be ready to share examples.
✨Tip Number 3
Prepare for client-facing scenarios! Since this role involves working directly with stakeholders, practice explaining complex data concepts in simple terms. Being able to communicate effectively will set you apart from other candidates.
✨Tip Number 4
Apply through our website! We’re actively looking for talented ML Engineers, and applying directly gives you a better chance to stand out. Don’t miss out on the opportunity to join a fast-growing AI consultancy!
We think you need these skills to ace Principal Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Principal ML Engineer role. Highlight your hands-on experience in deploying ML systems and working with cloud platforms like AWS or Azure.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about getting models into production. Share specific examples of how you've tackled real-world problems and engaged with stakeholders to drive results.
Showcase Your Technical Skills:Don’t shy away from detailing your technical expertise in Python, statistical modelling, and machine learning techniques. We want to see how you’ve applied these skills in complex environments, especially if you have experience in regulated industries.
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 shows us you’re keen on joining our team!
How to prepare for a job interview at Immersum
✨Know Your Stuff
Make sure you brush up on your Python skills and be ready to discuss your experience with machine learning and statistical modelling. Be prepared to explain how you've taken models from idea to production, especially in regulated environments.
✨Showcase Your Pragmatism
During the interview, highlight your ability to focus on impact over elegance. Share examples of how you've owned problems end-to-end and how you've navigated challenges with stakeholders. This will show that you're a hands-on engineer who can deliver real results.
✨Understand the Client Perspective
Since this role involves client-facing interactions, practice explaining complex data insights in simple terms. Think about how you would communicate performance metrics and findings to non-technical stakeholders, as building trust is key.
✨Familiarise Yourself with Regulations
If you've worked in regulated environments, be ready to discuss your knowledge of standards like ISO 27001 or GxP. If not, do some research on these frameworks and be prepared to talk about how they might apply to the role.
