At a Glance
- Tasks: Design and implement cutting-edge AI solutions for biomedical research.
- Company: Causaly is revolutionising scientific research with its AI platform, backed by top VCs.
- Benefits: Enjoy competitive pay, private health insurance, 25 days holiday, and a personal development budget.
- Other info: We celebrate diversity and welcome applicants from all backgrounds.
- Why this job: Join a mission-driven team transforming healthcare with AI and make a real impact.
- Qualifications: Master's degree in a relevant field and 5+ years of AI/ML experience required.
The predicted salary is between 48000 - 84000 £ per year.
Overview
In this role you will design and deliver end-to-end AI/ML solutions that power biomedical research at scale. You will work across the product and engineering teams to ship AI-powered features that improve researchers’ workflows and decision making. The position blends cutting-edge techniques with pragmatic, reliable software practices to create trustworthy, production-ready systems. You’ll contribute to a mission-driven platform that accelerates scientific breakthroughs for health. This is an opportunity to shape how biomedical professionals explore knowledge and solve real-world health challenges.
Pay / Benefits
- Competitive compensation
- Private medical and dental insurance
- Life insurance (4x salary)
- Personal development budget
- Wellbeing budget
- 25 days holiday + bank holidays + your birthday off
Responsibilities
- Design and implement ML/AI features end-to-end from ideation to deployment and monitoring
- Apply modular, testable software engineering practices (CI/CD, testing, observability) to AI systems
- Evaluate ML approaches (classical, NLP, LLM-based, agentic) to balance speed, cost, and performance
- Collaborate with product, design, and other engineers to scope work and ship user-facing AI features
- Document architectures and decisions to enable alignment and onboarding across teams
- Champion data quality, dataset versioning, evaluation, and real-world performance tracking
- Mentor junior AI engineers and cross-functional teammates to grow a high-trust, high-performance team culture
- Stay up-to-date with research and tools and translate insights into product opportunities
- Contribute to knowledge sharing through internal channels and technical talks
Key requirements
- Master's degree in Computer Science, Electrical Engineering or related field
- 5+ years of experience building AI/ML systems in production
- Proficiency in Python and frameworks such as PyTorch, TensorFlow, or LangChain with model packaging into APIs
- Clear, structured communication
- Collaborative mindset in cross-functional teams
- Mentorship and leadership potential
- ML/AI system design and deployment
- PyTorch, TensorFlow, LangChain
- LLMs, embeddings, prompt engineering, fine-tuning
Senior AI Engineer in London employer: Causaly
Causaly is an exceptional employer for the Life Sciences Strategy Director role, offering a dynamic work culture that fosters innovation and collaboration at the intersection of science and technology. With a competitive compensation package, comprehensive benefits including private medical and dental insurance, and a strong emphasis on personal development, employees are empowered to make a meaningful impact in the life sciences sector. The company's commitment to diversity and inclusion, alongside opportunities for accelerated career growth within an international team, makes it an attractive place for professionals seeking to drive scientific breakthroughs.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI Engineer in London
✨Tip Number 1
Familiarise yourself with the latest advancements in AI and ML, particularly in the biomedical field. This will not only help you understand the challenges Causaly is tackling but also allow you to discuss relevant topics during interviews.
✨Tip Number 2
Engage with the Causaly community by following their blog and participating in discussions on platforms like LinkedIn or Twitter. This shows your genuine interest in their work and can provide valuable insights into their culture and projects.
✨Tip Number 3
Prepare to demonstrate your technical skills through practical examples. Be ready to discuss specific projects where you've implemented AI/ML solutions, focusing on the impact of your work and how it aligns with Causaly's mission.
✨Tip Number 4
Network with current or former employees of Causaly to gain insider knowledge about the company culture and expectations. This can provide you with unique insights that can be beneficial during the interview process.
We think you need these skills to ace Senior AI Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights relevant experience in AI and ML systems, particularly in production environments. Emphasise your proficiency in Python and any frameworks like PyTorch or TensorFlow that are mentioned in the job description.
Craft a Compelling Cover Letter:In your cover letter, express your passion for transforming biomedical research through AI. Mention specific projects or experiences that align with Causaly's mission and how you can contribute to their goals.
Showcase Your Technical Skills:Include examples of your work with LLMs, data collection, and model evaluation. If you have experience with MLOps or LLMOps frameworks, make sure to highlight this as it’s a plus for the role.
Demonstrate Collaboration Experience:Causaly values teamwork, so provide examples of how you've successfully collaborated with cross-functional teams. Highlight any experience working with product managers, designers, or other engineers to deliver AI features.
How to prepare for a job interview at Causaly
✨Showcase Your Technical Skills
Be prepared to discuss your experience with AI/ML systems in detail. Highlight specific projects where you designed and implemented solutions, focusing on the technologies you used, such as Python, PyTorch, or TensorFlow.
✨Demonstrate Collaboration Experience
Causaly values teamwork, so share examples of how you've worked closely with product managers, designers, and other engineers. Discuss how you scoped work and aligned on success metrics to deliver user-facing features.
✨Prepare for Technical Questions
Expect questions about system behaviour, model performance, and observability. Be ready to explain your thought process when choosing between classical ML techniques and LLM-based solutions, and how you balance trade-offs.
✨Communicate Clearly and Confidently
Effective communication is key. Practice articulating your architectural decisions and be open to engaging in technical debates. Clear documentation skills will also be a plus, so mention any relevant experiences.
