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For Companies At a Glance
- Tasks: Build and deliver cutting-edge ML systems for global investment decisions.
- Company: Join Cerberus, a dynamic team at the forefront of AI technology.
- Benefits: Competitive salary, hands-on experience, and opportunities for growth.
- Why this job: Make a real impact with your work in a fast-paced environment.
- Qualifications: STEM degree or relevant experience in machine learning or software engineering.
- Other info: Collaborative culture with mentorship from experienced engineers.
The predicted salary is between 30000 - 42000 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;
- 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.
AI Engineer Analyst 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
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow AI enthusiasts. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML systems or data pipelines. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and practising common interview questions. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace AI Engineer Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Engineer role. Highlight relevant projects, internships, and skills that align with the job description. We want to see how your experience connects with what we do at StudySmarter!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for our team. Keep it concise but engaging—show us your personality!
Showcase Your Projects: If you've worked on any cool AI or ML projects, make sure to mention them! Whether it's a personal project or something from an internship, we love seeing practical applications of your skills. Include links if possible!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to keep track of your application status. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Cerberus Capital Management
✨Know Your Stuff
Make sure you brush up on your technical fundamentals, especially around machine learning and Python. Be ready to discuss any projects you've worked on, particularly those involving ML systems or data pipelines. This will show that you have the practical experience to back up your academic knowledge.
✨Show Your Curiosity
Demonstrate your eagerness to learn and grow as an AI engineer. Prepare questions about the team’s current projects and the technologies they use. This not only shows your interest but also helps you gauge if the role aligns with your career goals.
✨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. Being able to communicate effectively can set you apart from other candidates.
✨Be Ready to Prototype
Think of a few ideas or projects you could prototype that relate to the role. Discussing how you would approach building a simple ML system or data pipeline can showcase your problem-solving skills and creativity, which are crucial for this position.