At a Glance
- Tasks: Architect and integrate multi-agent systems for biomedical data retrieval and interpretation.
- Company: Join Quantori, a dynamic team at the forefront of tech innovation.
- Benefits: Enjoy competitive pay, flexible hours, healthcare benefits, and continuous education.
- Other info: Mentorship opportunities and a supportive environment for professional growth.
- Why this job: Make a real impact in healthcare by developing cutting-edge ML models.
- Qualifications: Proficient in Python with experience in ML frameworks and multi-agent systems.
The predicted salary is between 48000 - 84000 £ per year.
We are looking for a skilled and motivated Senior ML Engineer to join our dynamic team at Quantori. This position requires architecting and integrating multi-agent systems to support biomedical data retrieval and interpretation for target ideation, focusing on agentic workflows and tool compatibility. The role will be supported by an internal data scientist, e.g., coding tasks, data exploration, testing, etc.
Location: USA, UK, EU
Responsibilities:
- Architect multi-agent systems (supervisor, router, reasoning flow)
- Develop/tune ML models
- Ensure reproducibility, documentation
- Communicate ML findings to clinical research
- Integrate MCP server (potential)/other tools; ensure workflow compatibility
- Develop agent decision policies
- Diagnose reasoning gaps, refine routing logic
What we expect:
- Proficiency in Python programming and experience with Python data science frameworks
- Familiarity with common ML frameworks (e.g., PyTorch, Keras) and libraries (e.g., NumPy, scikit-learn)
- Experience with LLM agents including tool using and reasoning, for instance, the combination of RAG solution and code interpreter
- Experience with Multi-agent system architecture (e.g., LangGraph, ToolUniverse)
- Experience with MCP tool integration and orchestration
- Experience with API design and integration
- Experience with tool wrapping and interoperability
- Experience with Error handling and debugging (agentic workflows, logs)
- Experience with Modular system design, performance constraints
- Upper-Intermediate or higher level of English proficiency
- Ability to work with external clients and strong communication skills, including presenting in webinars and conferences
- Ability to mentor team members and assist in their professional development
- Quick learner with the ability to adapt to new technologies, frameworks, and algorithms
Preferred Skills:
- Bioinformatics/drug development lifecycle domain knowledge
- Knowledge Graphs (cypher)
- Background in clinical development, health analytics, biostatistics
- Real-world data experience (EHR, claims) and familiarity with medical coding systems (ICD-10, Snomed, RxNorm, CPT, NDC)
We offer:
- Competitive compensation
- Remote or office work
- Flexible working hours
- Healthcare benefits: medical insurance and paid sick leave
- Continuous education, mentoring, and professional development programs
- A team with excellent tech expertise
- Certifications paid by the company
Senior ML Engineer/Data Scientist in Cambridge employer: Quantori
At Quantori, we pride ourselves on being an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration. Our commitment to employee growth is evident through continuous education and mentoring programmes, alongside competitive compensation and flexible working arrangements. Join us in the USA, UK, or EU, where you will have the opportunity to work with cutting-edge technology in a supportive environment that values your contributions and professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Engineer/Data Scientist in Cambridge
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML and data science. 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 practising common ML questions and coding challenges. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior ML Engineer/Data Scientist in Cambridge
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior ML Engineer role. Highlight your experience with Python, ML frameworks, and any relevant projects that showcase your skills in multi-agent systems and biomedical data.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how your background aligns with our mission at Quantori. Don’t forget to mention your experience with tool integration and API design!
Showcase Your Communication Skills:Since you'll be communicating ML findings to clinical research, it's important to demonstrate your communication skills in your application. Share examples of how you've presented complex information clearly in the past.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Quantori
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the ML frameworks mentioned in the job description, like PyTorch and Keras. Brush up on your knowledge of multi-agent systems and be ready to discuss how you've used these technologies in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've diagnosed reasoning gaps or refined routing logic in your previous roles. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your analytical skills.
✨Communicate Clearly and Confidently
Since the role involves presenting findings to clinical research teams, practice explaining complex concepts in simple terms. You might even want to do a mock presentation to a friend or colleague to get comfortable with your delivery.
✨Demonstrate Your Mentorship Abilities
Think of instances where you've mentored others or contributed to their professional development. Be prepared to share how you can support team members in their growth, as this is a key expectation for the role.
