Senior AI Engineer

Senior AI Engineer

London Full-Time 48000 - 84000 £ / year (est.) No home office possible
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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.
  • 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.
  • Other info: We celebrate diversity and welcome applicants from all backgrounds.

The predicted salary is between 48000 - 84000 £ per year.

Founded in 2018, Causaly’s AI platform for enterprise-scale scientific research redefines the limits of human productivity. We give humans a powerful new way to find, visualize and interpret biomedical knowledge and automate critical research workflows, accelerating solutions for some of the greatest health challenges we face.

We work with some of the world\’s largest biopharma companies and institutions on use cases spanning Drug Discovery, Safety and Competitive Intelligence. You can read more about how we accelerate knowledge acquisition and improve decision making in our blog posts here: Blog – Causaly

We are backed by top VCs including ICONIQ, Index Ventures, Pentech and Marathon.

About the team

We’re hiring AI engineers to help us transform research outcomes in biomedical sciences. We utilise generative AI and agents to help scientists find novel connections and insights from biomedical literature and datasets.

Delivering a faster research experience is not just a matter of picking the latest LLM model. Getting accurate, scientifically relevant, trustworthy and meaningful results relies on building trust with our users that comes from integrating proper levels of guardrails, biomedical curation, consistent update cycles and robust deployment practices that make our platform a one-stop shop for a researcher’s needs.

We’re looking for AI engineers who will champion this cause and help us apply AI to transform the way biomedical professionals carry out their research and day-to-day exploration.

What you’ll be doing

· Design and implement ML/AI solutions end-to-end, from the idea and data exploration phase to deployment and monitoring, balancing cutting-edge techniques with pragmatism to deliver measurable impact.

· Apply strong software engineering principles, such as modularity, testing, code reviews, CI/CD and observability, to ensure AI systems are reliable, maintainable, production-ready and can be readily adapted to future developments.

· Choose the right approach for the problem at hand, evaluating classical ML and NLP techniques, LLM-based solutions, and agentic solutions to balance trade-offs between speed, cost, complexity, interpretability, and performance.

· Collaborate closely with product, design, and other engineering teams to scope work, align on success metrics, and incrementally ship improvements in user-facing features powered by AI.

· Document system architectures and decision rationale early and clearly, enabling alignment across teams and accelerating onboarding and iteration.

· Champion model and data quality, including dataset versioning, robust evaluation, fairness/bias assessment, and real-world performance tracking.

· Mentor junior AI engineers and cross-functional teammates, sharing best practices in modelling, coding, maintaining and integrating product features, and helping grow a high-trust, high-performance team culture.

· Stay up-to-date with emerging research and tools, distilling key insights and bringing back relevant innovations to elevate team capabilities and product opportunities.

· Contribute to a culture of knowledge sharing, through company-wide Slack channels, Show and Tell presentations and technical deep-dives.

What experience you’ll need to be successful

· A master\’s degree or above in Computer Science, Electrical Engineering or a related field.

· 5+ years of experience building AI/ML systems in production environments, including ownership of key lifecycle stages: data collection, modeling, evaluation, deployment, and monitoring.

· Proficiency in Python and modern ML and agentic frameworks such as PyTorch, TensorFlow, or LangChain, with experience packaging models into APIs or integrating them into applications.

· A solid understanding of LLMs for natural language processing applications, including topics such as embeddings, prompt engineering and fine-tuning.

· Strong software engineering foundations such as version control, unit/integration testing, CI/CD, containerization plus a mindset of building for reliability and scale.

· Experience working in product-focused teams, collaborating with designers, engineers, and PMs, to scope and ship AI features iteratively

· Ability to reason about system behavior end-to-end, including model performance, latency, and observability, and how these impact user experience.

· Clear, structured communicator, comfortable documenting and defending architectural decisions and engaging in thoughtful technical debate.

Not required, but it’s a plus if you also have:

· Experience with MLOps/LLMOps frameworks and best practices

· A PhD in Computer Science, Electrical Engineering or a related field.

· A background or work experience in life-sciences, health-tech, or other data-intensive domains

Benefits

Competitive compensation package

???? Private medical & dental insurance

Life insurance (4 x salary)

Personal development budget

Individual wellbeing budget

25 days holiday plus bank holidays

Your birthday off!

Potential to have real impact and accelerated career growth as a member of an international team that\’s building a transformative AI product.

We are on a mission to accelerate scientific breakthroughs for ALL humankind, and we are proud to be an equal opportunity employer. We welcome applications from all backgrounds and fairly consider qualified candidates without regard to race, ethnic or national origin, gender, gender identity or expression, sexual orientation, disability, neurodiversity, genetics, age, religion or belief, marital/civil partnership status, domestic / family status, veteran status or any other difference.

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Senior AI Engineer employer: Causaly

Causaly is an exceptional employer for Senior AI Engineers, offering a dynamic work culture that fosters innovation and collaboration in the heart of the biomedical research sector. With competitive compensation, comprehensive benefits including private medical insurance and personal development budgets, and a commitment to employee growth, Causaly empowers its team to make a meaningful impact on global health challenges while enjoying a supportive and inclusive environment.
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Contact Detail:

Causaly Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Senior AI Engineer

✨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

Machine Learning Engineering
Natural Language Processing (NLP)
Generative AI
Software Development Principles
Python Programming
PyTorch
TensorFlow
LangChain
API Development
Data Collection and Management
Model Evaluation and Monitoring
CI/CD Practices
Containerization
Version Control Systems
System Architecture Documentation
Mentoring and Team Leadership
Collaboration with Cross-Functional Teams
Problem-Solving Skills
Communication Skills
Understanding of LLMs
Bias and Fairness Assessment

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.

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