At a Glance
- Tasks: Join our AI Research team to develop and evaluate cutting-edge AI models.
- Company: PwC, a leader in innovative AI solutions with a collaborative culture.
- Benefits: Flexible working, private medical cover, and six volunteering days annually.
- Other info: Dynamic environment with opportunities for personal and professional growth.
- Why this job: Shape the future of AI research and make a real impact on client projects.
- Qualifications: Strong experience in ML experimentation and proficiency in Python required.
The predicted salary is between 36000 - 60000 £ per year.
About the Role
You’ll join our AI Research team as a Machine Learning Engineer – Senior Associate, helping to shape and evolve the model benchmarking and experimentation capabilities that underpin AI delivery across PwC and our clients. You’ll work within a highly collaborative applied research environment that values curiosity, technical rigour and practical problem solving. In this role, you will develop frameworks and experimentation workflows used to evaluate emerging AI models, driving evidence-based decisions for client engagements. We’re looking for someone who enjoys technical ownership, thrives in fast-moving environments and is motivated by building scalable, secure AI research infrastructure.
What Your Days Will Look Like
- You’ll play a key role in building and evolving our AI benchmarking and experimentation platforms, enabling robust and repeatable model evaluation.
- Your work will directly influence AI model selection and technical strategy across PwC projects and client engagements.
- Design and run end to end benchmarking workflows, from understanding client use cases, designing and running benchmarking strategies, and generating business ready insights.
- Build scalable evaluation frameworks, metrics and pipelines, combining hands-on engineering with continuous review of academic literature to ensure our evaluation strategies reflect leading research and best practice.
- Develop, maintain and improve experimentation infrastructure, ensuring robustness and production grade engineering.
- Produce clear, client ready insights and support technical demos and deep dive sessions.
This Role Is For You If
- You have strong hands-on experience in ML or LLM experimentation using structured evaluation frameworks.
- You are highly proficient in Python, including asynchronous programming, multithreading and writing maintainable code.
- You have experience deploying ML workloads to cloud platforms (Azure, AWS or GCP), with familiarity in CI/CD and containerisation (Docker/Podman).
- You have applied knowledge of statistics and experimental design and can translate findings into actionable recommendations.
- You are comfortable managing fast-moving workstreams and operating autonomously.
- You are motivated by ownership and excited by the opportunity to shape AI research platforms that directly impact client engagements.
What You’ll Receive From Us
No matter where you may be in your career or personal life, our benefits are designed to add value and support, recognising and rewarding you fairly for your contributions. We offer a range of benefits including empowered flexibility and a working week split between office, home and client site; private medical cover and 24/7 access to a qualified virtual GP; six volunteering days a year and much more.
Machine Learning Engineer employer: PwC UK
Contact Detail:
PwC UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning space, especially those at PwC. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects and experiments. This is your chance to demonstrate your hands-on experience and technical ownership.
✨Tip Number 3
Prepare for technical interviews by brushing up on Python and ML concepts. Practice coding challenges and be ready to discuss your past projects in detail.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Machine Learning Engineer role. Highlight your hands-on experience with ML experimentation and any relevant projects you've worked on. We want to see how you can contribute to our AI Research team!
Craft a Compelling Cover Letter: Your cover letter is your chance to show us your personality and passion for AI research. Share why you're excited about this role and how your background aligns with our needs. Don’t forget to mention your experience with Python and cloud platforms!
Showcase Your Technical Skills: In your application, be sure to highlight your proficiency in Python, especially with asynchronous programming and multithreading. If you've deployed ML workloads or have experience with CI/CD and containerisation, let us know! We love seeing those technical chops.
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 at StudySmarter!
How to prepare for a job interview at PwC UK
✨Know Your ML Fundamentals
Brush up on your machine learning concepts, especially around model benchmarking and experimentation. Be ready to discuss specific frameworks you've used and how they relate to the role. This shows you’re not just familiar with the theory but can apply it practically.
✨Showcase Your Python Skills
Since proficiency in Python is key, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot or discuss your previous projects. Make sure you can explain your thought process clearly and highlight any experience with asynchronous programming or multithreading.
✨Familiarise Yourself with Cloud Platforms
Understand the cloud platforms mentioned in the job description, like Azure, AWS, or GCP. Be prepared to discuss your experience deploying ML workloads and how you’ve utilised CI/CD and containerisation tools like Docker. This will show that you can hit the ground running.
✨Prepare for Problem-Solving Scenarios
Expect to tackle real-world problems during the interview. Think about how you would design and run benchmarking workflows based on hypothetical client use cases. Practising these scenarios can help you articulate your approach and demonstrate your problem-solving skills effectively.