At a Glance
- Tasks: Build and deliver cutting-edge ML systems that drive investment decisions.
- Company: Join a dynamic team at a leading global investment firm.
- Benefits: Competitive salary, hands-on experience, and rapid career growth.
- Other info: Collaborative culture with exposure to diverse projects and technologies.
- Why this job: Make a real impact with AI technology in a fast-paced environment.
- Qualifications: STEM degree or relevant experience in machine learning or software engineering.
The predicted salary is between 28800 - 48000 € per year.
As an early‐career AI Engineer at Cerberus, you'll join a small, high‐impact team building AI systems that power decision‐making across a global investment platform. You'll work alongside experienced AI engineers, data scientists, and technologists to deliver real products used by investment teams and portfolio companies.
This role is ideal for:
- PhD graduates in a STEM field with applied ML, optimization, or computational experience; or
- Bachelor's/Master's graduates with 1–2 years of industry experience or relevant internships in machine learning, data engineering, or software engineering.
You'll contribute to designing, implementing, and deploying production-grade ML systems—ranging from NLP pipelines to model‐driven workflow automation. You'll learn quickly, gain real ownership, and see your work make tangible business impact.
What You'll Do
- Build and deliver ML systems: Work with senior engineers to design, train, and deploy machine learning models and data-driven tools that support investment and operational decision-making.
- Contribute to real production deployments: Help integrate ML models into business workflows, build data pipelines, and support the rollout of AI applications across teams.
- Experiment and iterate: Prototype ideas, test assumptions, and rapidly evolve solutions based on real user feedback and real-world constraints.
- Learn modern tooling and practices: Gain hands-on experience with ML frameworks, cloud infrastructure, MLOps tools, and best practices for building scalable AI systems.
- Communicate clearly: Translate technical findings into clear, structured insights for collaborators across technical and business teams.
- Grow as an AI engineer: Develop skills across the full ML lifecycle—data processing, modelling, evaluation, deployment, and ongoing improvement.
Sample Projects You Might Work On
- GenAI for due diligence: Support the configuration, extension, and rollout of our in-house GenAI platform across investment teams. Work with senior engineers to customise workflows, analyse model outputs, and drive adoption.
- Automated Deal Sourcing Tools: Help build prototypes that extract signals from datasets and integrate with APIs to enrich leads. Support the creation of modular ML-driven components that can be used across investment strategies.
Your Experience: We don't expect candidates to have experience across all areas—what matters most is strong technical fundamentals, curiosity, and a willingness to learn quickly.
Foundational skills
- Degree in a STEM field.
- PhD candidates: applied research involving ML, optimisation, simulation, statistics, numerical methods, NLP, or related areas.
- Bachelor's/Master's: 1–2 years of industry experience or relevant internships in ML, software engineering, or data engineering.
Programming experience (especially Python)
- Experience writing clean, maintainable Python code.
- Applied AI experience such as exposure to LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.) and experience with small personal or internship projects building agents or AI-driven workflows.
- Agentic frameworks in Python is a plus but not required.
Data and analytical skills
- Comfortable working with data, performing analysis, and writing SQL queries.
- Experience building simple data pipelines or transformation workflows is a plus.
- Exposure to ML Ops or production systems (nice to have).
- Familiarity with tools like MLflow, Weights & Biases, or cloud platforms (Azure, AWS, or GCP).
- Experience deploying models via APIs or lightweight services is a bonus, not a requirement.
Software engineering basics
- Understanding of Git/GitHub/Azure DevOps, testing basics, and general good engineering practices.
Mindset
- Strong problem-solving skills.
- Curiosity and eagerness to learn.
- Pragmatic, impact-driven approach.
- Ability to work collaboratively in a fast-paced environment.
About Us
We are a growing team of AI specialists—data scientists, ML engineers, software engineers, and technology strategists—working to transform how a global investment firm with $65B+ in assets uses data and AI. We operate like a startup within the firm: fast, collaborative, and focused on delivering real value. Our work spans investment desks, portfolio companies, and core operations, giving early-career engineers wide exposure and the opportunity to grow rapidly.
AI Engineer Analyst in England employer: Cerberus Capital Management
At Cerberus, we pride ourselves on being an exceptional employer for early-career AI Engineers, offering a dynamic work environment that fosters collaboration and innovation. Our team operates like a startup within a global investment firm, providing ample opportunities for professional growth and hands-on experience with cutting-edge AI technologies. With a strong focus on delivering impactful solutions, you'll have the chance to contribute meaningfully to real-world projects while learning from seasoned experts in the field.
Contact Detail:
Cerberus Capital Management Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer Analyst in England
✨Tip Number 1
Network like a pro! Reach out to current AI engineers or alumni from your university who are in the field. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML or data engineering. 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 your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail—this is your chance to shine!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for fresh talent, and applying directly can give you a better shot at landing that dream role with us.
We think you need these skills to ace AI Engineer Analyst in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the AI Engineer role. Highlight any relevant projects or internships, especially those involving machine learning or data engineering.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI and how your background makes you a great fit for our team. Share specific examples of your work and what you hope to achieve in this role.
Showcase Your Technical Skills:Don’t shy away from listing your programming languages and tools you’re familiar with, especially Python and any ML frameworks. We love seeing practical applications of your skills, so include any personal projects or contributions to open-source.
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 Cerberus Capital Management
✨Know Your Stuff
Make sure you brush up on your technical fundamentals, especially in machine learning and Python programming. Be ready to discuss any projects you've worked on, particularly those involving ML models or data pipelines.
✨Show Your Curiosity
Demonstrate your eagerness to learn by asking insightful questions about the team’s projects and the technologies they use. This shows that you're not just interested in the role but also in growing as an AI engineer.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to translate your findings for both technical and non-technical team members, so clarity is key!
✨Be Ready to Prototype
Think of a few ideas you could prototype related to the role. Discussing how you would approach building a simple ML system or data pipeline can really impress the interviewers and show your hands-on mindset.
