At a Glance
- Tasks: Design and deploy cutting-edge AI and ML models in drug development.
- Company: Leading company in drug development with a focus on AI and ML.
- Benefits: Flexible working arrangements, competitive salary, and career growth opportunities.
- Other info: Collaborative environment with ownership in shaping AI strategy.
- Why this job: Shape the future of AI in healthcare and make a real impact.
- Qualifications: PhD in relevant field and 2-5 years of ML/AI experience.
The predicted salary is between 50000 - 70000 £ per year.
We are working with a company at the forefront of drug development and manufacturing. They turn drugs into usable forms, test them in humans, and manufacture the drugs. The company is now focused on investing in the Machine Learning and Artificial Intelligence space, and has several open positions in this area.
As an AI/ML Engineer, you’ll be involved across the full AI lifecycle - from getting the data in, through to building and deploying models, right through to monitoring how they perform in production. Internally, you’ll be seen as a go-to technical expert, helping to ensure best practices around responsible AI, model governance, and compliance are followed. You’ll also work cross-functionally with departments across the business from product managers to data engineers, to turn requirements into real-world AI solutions.
Key Responsibilities- Design, develop, and deploy AI and machine learning models, finetune models as required.
- Build and maintain scalable ML pipelines and infrastructure for classical ML and deep learning.
- Deploy models to production using containerisation, CI/CD, and MLOps toolsets.
- Develop LLM-based tools using prompt engineering, retrieval, and embedding pipelines for knowledge retrieval and workflow assistance.
- Embed responsible AI practices and compliance in all solutions.
- Collaborate with cross-functional teams to translate requirements into technical solutions.
- Communicate complex technical concepts in clear terms to technical and non-technical stakeholders.
Ideally, you will have a PhD in a relevant field and 2-5 years’ experience in ML/AI engineering or data science roles. Previous experience working with biological or chemical data and understanding of the drug development ecosystem is preferred.
Proven track record of building, deploying, and maintaining production-grade models. Strong coding background in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with cloud platforms and ML infrastructure (ideally AWS SageMaker, MLflow). Experience with LLMs, vector search, or retrieval-augmented systems. Excellent communication skills; able to work cross-functionally with stakeholders of both technical and non-technical backgrounds.
This is a great opportunity for someone looking to take the next step in their career; you will get ownership and the chance to help shape a company’s AI and ML strategy. You’ll need to be self-motivated and enjoy the challenge of building things from scratch. Apply today to be considered.
Machine Learning Engineer employer: KEMIO Consulting
As a leading innovator in drug development and manufacturing, this company offers an exceptional work environment for Machine Learning Engineers in Cambridge, with flexible working arrangements that promote a healthy work-life balance. Employees benefit from a collaborative culture that encourages cross-functional teamwork, providing ample opportunities for professional growth and the chance to influence the company's AI and ML strategy. With a focus on responsible AI practices and cutting-edge technology, this role is perfect for those looking to make a meaningful impact in the healthcare sector.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML and AI. 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 soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical folks.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications come directly from candidates who are excited about joining us. Plus, it shows you're genuinely interested in being part of our team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your relevant experience, especially in AI/ML projects and any work with biological or chemical data. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and ML, and how you can contribute to our mission. Keep it concise but impactful – we love a good story!
Showcase Your Projects:If you've worked on any interesting ML projects, make sure to mention them! Whether it's deploying models or building pipelines, we want to know what you've done. Include links to your GitHub or portfolio if you have one!
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. Plus, it’s super easy!
How to prepare for a job interview at KEMIO Consulting
✨Know Your Stuff
Make sure you brush up on your machine learning concepts and frameworks like TensorFlow and PyTorch. Be ready to discuss your past projects, especially those involving biological or chemical data, as this will show your relevance to the role.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've tackled challenges in previous roles. Think of specific examples where you designed, developed, or deployed ML models, and be ready to explain your thought process and the impact of your solutions.
✨Communicate Clearly
Since you'll be working with both technical and non-technical stakeholders, practice explaining complex concepts in simple terms. This will demonstrate your ability to bridge the gap between different teams and ensure everyone is on the same page.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's AI strategy and how they approach responsible AI practices. This shows your genuine interest in the role and helps you assess if the company aligns with your values and career goals.
