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For Companies At a Glance
- Tasks: Join us to design and build cutting-edge LLM agents for life sciences.
- Company: Be part of a dynamic team pushing the boundaries of machine learning.
- Benefits: Enjoy flexible working options and a collaborative culture.
- Why this job: Work on impactful projects that blend science and technology in a fast-paced environment.
- Qualifications: BSc, MSc or PhD in a relevant field with strong Python and ML skills required.
- Other info: Ideal for curious minds eager to learn and innovate in AI.
The predicted salary is between 48000 - 72000 £ per year.
Role Description
We are looking for a machine learning engineer with strong data science expertise to join the team working on large language models for life and natural science problems. Work involves building agentic workflows where LLMs reason, plan and act, as well as developing pipelines to train and fine-tune models. LangGraph is our main framework for agent development; knowledge of other agent stacks is a plus
Key Responsibilities
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Design and build multi-step LLM agents with LangGraph and similar frameworks
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Create data and ML pipelines for continual pre-training, supervised fine-tuning and RL alignment
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Deploy models and retrieval services on containerised infrastructure with reliable CI/CD
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Monitor and improve agent performance with Weights & Biases and internal dashboards
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Collaborate with scientists and engineers to turn research ideas into working products
Required Qualifications
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BSc, MSc or PhD in Computer Science, Data Science or a related field
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Strong Python skills with PyTorch, HuggingFace Transformers and Datasets
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Proven track record fine-tuning and serving large language models in real-world settings
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Hands-on experience building pipelines with reinforcement-learning algorithms such as PPO and GRPO
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Competence with containers, automated testing and software-engineering best practice
Useful Skills
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Basic experience with GCP and infrastructure-as-code workflows
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Hands-on experience using vector, graph and relational databases, plus SQL and data modelling
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Experience with multimodal models and emerging agent protocols such as MCP and A2A
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Ability to implement model safety and guard-rail measures
Personal Attributes
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Team player with clear communication
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Analytical and detail-oriented problem solver
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Curious and quick to learn new methods
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Comfortable in a fast-moving research environment
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Committed to delivering maintainable, reliable software
#LI-SS2 #J-18808-Ljbffr
Machine Learning Engineer / Data Scientist LLM Agents (London) employer: Springer Nature
Contact Detail:
Springer Nature Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer / Data Scientist LLM Agents (London)
✨Tip Number 1
Familiarise yourself with LangGraph and similar frameworks before applying. Understanding how to design and build multi-step LLM agents will give you a significant edge in the interview process.
✨Tip Number 2
Brush up on your Python skills, especially with libraries like PyTorch and HuggingFace Transformers. Being able to demonstrate your hands-on experience with these tools can set you apart from other candidates.
✨Tip Number 3
Gain practical experience with reinforcement-learning algorithms such as PPO and GRPO. Having real-world examples of how you've implemented these techniques will showcase your expertise and problem-solving abilities.
✨Tip Number 4
Network with professionals in the field and engage in relevant online communities. This can provide insights into current trends and challenges in machine learning, which can be beneficial during interviews.
We think you need these skills to ace Machine Learning Engineer / Data Scientist LLM Agents (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning, data science, and any relevant frameworks like LangGraph. Emphasise your Python skills and any hands-on experience with large language models.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about working with LLMs in life and natural sciences. Mention specific projects or experiences that demonstrate your ability to design and build multi-step agents.
Showcase Relevant Projects: If you have worked on projects involving reinforcement learning, model fine-tuning, or containerised infrastructure, be sure to include these in your application. Provide links to your GitHub or portfolio if possible.
Highlight Collaboration Skills: Since the role involves collaboration with scientists and engineers, mention any previous teamwork experiences. Highlight your communication skills and how you've successfully turned research ideas into practical applications.
How to prepare for a job interview at Springer Nature
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, PyTorch, and HuggingFace Transformers. Highlight specific projects where you've fine-tuned large language models and built ML pipelines, as this will demonstrate your hands-on expertise.
✨Understand the Role of LLMs
Familiarise yourself with how large language models function, especially in the context of life and natural sciences. Be ready to explain how you would design multi-step LLM agents using LangGraph or similar frameworks.
✨Prepare for Problem-Solving Questions
Expect to face analytical questions that assess your problem-solving skills. Think about past challenges you've encountered in machine learning projects and how you overcame them, particularly in fast-paced environments.
✨Demonstrate Collaboration Skills
Since the role involves working closely with scientists and engineers, be ready to discuss your teamwork experiences. Share examples of how you've effectively communicated complex ideas and collaborated on projects to turn research into practical applications.
