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For Companies 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.
- Why this job: Make a real impact with AI while learning from industry experts.
- Qualifications: STEM degree or relevant experience in machine learning or software engineering.
- Other info: Collaborative startup-like environment with exposure to diverse projects.
The predicted salary is between 36000 - 60000 ÂŁ 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.
(Both examples are reframed so junior team members contribute meaningfully but are not expected to independently lead full workstreams.)
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 London employer: Cerberus Capital Management
Contact Detail:
Cerberus Capital Management Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer Analyst in London
✨Tip Number 1
Network like a pro! Reach out to current employees at Cerberus or similar companies on LinkedIn. Ask them about their experiences and any tips they might have for landing a role like the AI Engineer Analyst.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to machine learning and AI. This could be anything from personal projects to internships. Make sure to highlight how your work has made an impact.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills, especially in Python. Practice common ML algorithms and be ready to discuss your thought process when solving problems.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace AI Engineer Analyst in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI, machine learning, and any projects you've worked on. We want to see how your skills align with the role, so don’t be shy about showcasing your best work!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about the AI Engineer role and how your background makes you a great fit. Keep it concise but impactful—let us know what drives your passion for AI.
Showcase Your Projects: If you've worked on any personal or academic projects related to AI or machine learning, make sure to mention them! We love seeing practical applications of your skills, so include links or descriptions that highlight your contributions.
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 serious about joining our team at StudySmarter!
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 relevant projects or internships you've worked on, as this will show your practical experience and understanding of the field.
✨Show Your Curiosity
During the interview, express your eagerness to learn and grow. Ask insightful questions about the team's projects and the technologies they use. This not only demonstrates your interest but also helps you gauge if the role aligns with your career goals.
✨Communicate Clearly
Practice translating complex technical concepts into simple terms. You might be asked to explain your past work or a project, so being able to communicate your ideas clearly will impress the interviewers and show that you can collaborate effectively with both technical and non-technical teams.
✨Be Ready to Experiment
Since the role involves prototyping and iterating on solutions, be prepared to discuss how you've approached problem-solving in the past. Share examples where you've tested assumptions and adapted based on feedback, showcasing your ability to think critically and adapt quickly.
