At a Glance
- Tasks: Lead a team to transform research into scalable software solutions and manage cutting-edge AI infrastructure.
- Company: Join a pioneering tech firm at the forefront of AI research and engineering.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on innovation and collaboration in a fast-paced environment.
- Why this job: Make a real impact by bridging research and production in the AI landscape.
- Qualifications: 7+ years in software engineering with leadership experience and deep learning expertise.
The predicted salary is between 80000 - 100000 € per year.
Requirements
- You are not just a manager; you are a builder who understands the unique challenges of Deep Learning infrastructure.
- You bring a product-oriented lens to engineering — you've led teams that didn't just ship features but owned outcomes, defined success criteria, and iterated based on user feedback, whether those users were internal researchers or external customers.
- Education: BSc, MSc, or PhD in Computer Science, Software Engineering, or a related field.
- Experience: 7+ years of software engineering experience, with at least 3+ years leading or managing engineering teams.
- Product-Oriented Leadership: Demonstrated experience leading engineering efforts with a product mindset, defining roadmaps tied to user/business outcomes, working cross-functionally with product and business stakeholders, and making build-vs-buy decisions grounded in impact rather than purely technical preference.
- Deep proficiency in Python and modern software development practices.
- Hands-on experience with Distributed Training infrastructure (Multi-node GPU training, Kubernetes, vLLM).
- Familiarity with Deep Learning frameworks (PyTorch).
- Experience with MLOps tools and experiment tracking (e.g., ClearML, MLFlow, Weights & Biases).
- Research Fluency: Ability to read technical research papers and translate them into engineering requirements. You don't need to write the paper, but you need to understand the architecture required to support it.
- Operational Mindset: Experience managing cloud resources (AWS/Azure/GCP) and optimizing for cost/performance.
- (Desirable) Experience working in a Research Lab or '0-to-1' innovation environment.
- (Desirable) Experience owning the end-to-end lifecycle of an internal developer platform or ML tooling product, including defining adoption metrics and iterating based on user research.
- (Desirable) Background in Platform Engineering.
- (Desirable) Experience contributing to open-source LLM or NLP libraries.
What the job involves
- As the Manager of Research Engineering, you will sit at the critical intersection of cutting-edge academic research and robust software engineering. You will lead the team responsible for turning experimental code into scalable assets and ensuring our researchers have the compute and tooling required to compete with the world’s top AI labs.
- Engineering Leadership: Manage, mentor, and grow a team of Research Engineers. You will foster a culture of engineering rigor (code quality, testing, CI/CD) within a fast-paced, experimental research environment.
- Infrastructure & LLMOps Strategy: Own the technical strategy for our LLM training and inference infrastructure. This includes managing distributed compute clusters (LambdaLabs/AWS), orchestration platforms (e.g., ClearML, Kubernetes), and data pipelines.
- Vendor & Governance Management: Lead the evaluation and onboarding of external technology vendors. You will act as the primary liaison with Sourcing, Procurement, Risk, and Privacy teams to ensure our tooling infrastructure is compliant, secure, and procured efficiently, unblocking the research team from administrative overhead.
- Bridge Research & Production: Act as the primary translator between the Foundational Research team and the wider Platform/Engineering organizations. You will ensure that research innovations are architected in a way that allows them to be successfully handed off to production teams.
- Product-Minded Engineering: Drive a product-oriented mindset within the research engineering team, ensuring that infrastructure, tooling, and experimental frameworks are designed not just for technical excellence but with clear user outcomes in mind. Champion practices like defining success metrics for internal platforms, gathering feedback from product partners, and prioritising work based on impact to downstream product value.
- Operational Rigor: Remove ambiguity for your team by translating high-level research goals into concrete engineering roadmaps. You will implement observability, alerting, and resource management strategies to ensure efficient use of our massive compute budget.
- Hands-on Contribution: While primarily a leader, you are willing to roll up your sleeves to review code, debug distributed training failures, and architect complex system integrations.
Manager of Research Engineering (Foundational Research) in London employer: Deepstreamtech
As a leading innovator in the field of Deep Learning, our company offers an exceptional work environment where creativity and technical excellence thrive. We prioritise employee growth through mentorship and hands-on leadership opportunities, fostering a culture that values collaboration and continuous improvement. Located in a vibrant tech hub, we provide access to cutting-edge resources and a dynamic team dedicated to pushing the boundaries of AI research and engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Manager of Research Engineering (Foundational Research) in London
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio or GitHub, make sure it’s up-to-date. Let your work speak for itself and impress potential employers.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to the role. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨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!
We think you need these skills to ace Manager of Research Engineering (Foundational Research) in London
Some tips for your application 🫡
Show Your Passion for Deep Learning:When writing your application, let your enthusiasm for Deep Learning shine through! We want to see how you’ve tackled challenges in this field and how your experience aligns with our mission at StudySmarter.
Highlight Your Leadership Experience:Make sure to showcase your leadership skills and experiences. We’re looking for someone who has not only managed teams but has also driven product-oriented outcomes. Share specific examples of how you've led teams to success!
Be Clear About Your Technical Skills:Don’t hold back on detailing your technical expertise! We need to know about your proficiency in Python, distributed training infrastructure, and any MLOps tools you’ve used. The more specific you are, the better we can see how you fit into our team.
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 from our team. Let’s get started on this journey together!
How to prepare for a job interview at Deepstreamtech
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical requirements listed in the job description. Brush up on your Python skills, and be ready to discuss your experience with distributed training infrastructure and MLOps tools. Being able to speak confidently about these topics will show that you’re not just a manager but a builder who understands the tech.
✨Showcase Your Leadership Style
Prepare examples of how you've led teams in the past, especially in product-oriented environments. Think about specific instances where you defined success criteria or iterated based on user feedback. This will demonstrate your ability to foster a culture of engineering rigor and mentor your team effectively.
✨Bridge the Gap Between Research and Production
Be ready to discuss how you’ve successfully translated research innovations into production-ready solutions. Highlight any experiences where you acted as a liaison between research and engineering teams, ensuring smooth handoffs and alignment on goals. This is crucial for the role, so make it a focal point in your interview.
✨Prepare Questions That Matter
Think of insightful questions to ask your interviewers about their current challenges and future goals. This shows your genuine interest in the role and helps you gauge if the company aligns with your vision. Ask about their approach to managing cloud resources or how they define success metrics for their internal platforms.
