At a Glance
- Tasks: Lead a high-performing AI engineering team to deliver innovative agent capabilities.
- Company: Join a cutting-edge tech company focused on AI solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with a strong focus on collaboration and experimentation.
- Why this job: Make a real impact by shaping the future of AI technology.
- Qualifications: Experience in leading engineering teams and delivering complex AI projects.
The predicted salary is between 80000 - 100000 £ per year.
Requirements
- Led the technical delivery of complex, ambiguous products or systems in a domain without established playbooks, breaking down unknowns into testable hypotheses that led to meaningful shipped outcomes.
- Built and iterated on a structured experimentation or discovery framework (for example, spikes, A/B tests, or eval-driven loops) that reduced feasibility risk and accelerated decision making.
- Owned and delivered a DevEx or platform roadmap that materially improved engineering velocity, such as introducing CI/CD, improving testing and observability, or simplifying developer workflows.
- Managed or technically led a high-performing engineering team, creating psychological safety while holding a high bar for quality, delivery, and ownership of outcomes.
- Shipped AI / ML powered features or systems at production scale, working through challenges around reliability, observability, cost, and user trust, not just model performance in isolation.
- Collaborated closely with cross-functional partners (such as Product, Design, and Research / Data Science) to define strategy, shape roadmaps, and make trade-offs between delivery, debt, and foundational investment.
What the job involves
- Our Agent teams are building and scaling cutting-edge AI agents that power real customer workflows, from autonomous analysis to interactive assistance and user-facing automation.
- You will sit at the heart of translating ambitious product ideas and performance targets into robust, production-ready agent capabilities that materially move the needle for our customers.
- This group operates like a highly aligned, elite squad: tight collaboration between Engineering, Product, Design, and Research Engineering; fast feedback loops; and a bias towards experimentation over theory.
- Everything we build is novel, so we treat uncertainty as a design constraint, using formal explore / exploit practices to discover what works, kill what does not, and double down on impact.
- As Engineering Manager, you will own the technical delivery of one of our Agent roadmaps (Actuarial Agent, Ingestion Agent, or Underwriting Agent), leading AI Engineers to ship features that meet real customer requirements and close gaps surfaced through our eval pipelines.
- Your impact will be visible in the capabilities customers rely on day to day, and in the compounding velocity of a team that can repeatedly solve problems no one has solved before.
- Owning end-to-end technical delivery for one Agent team, turning customer requirements and eval-driven performance gaps into shipped capabilities that measurably improve agent success rates, latency, and reliability.
- Designing and institutionalising an explore / exploit operating model for your team, using timeboxed spikes, clear kill criteria, and exploitation triggers to reduce feasibility risk and increase the hit-rate of successful agent solutions.
- Partnering with Product, Design, and Research Engineering to co-design solutions, rapidly assessing feasibility for novel use cases, and steering the roadmap as you learn from real-world performance and customer feedback.
- Building and maintaining a DevEx roadmap focused on engineering velocity, investing in tooling, CI/CD, testing infrastructure, and observability so the team can safely increase deployment frequency and reduce cycle times.
- Coaching and mentoring AI Engineers to take on larger, more complex problems, growing technical leaders who can confidently drive designs, lead research spikes, and own critical areas of the agent stack.
- Creating a culture of high trust and high accountability, where experimentation is encouraged, data and evals guide decisions, and the team takes collective responsibility for the quality and performance of agents in production.
Engineering Manager/Team Lead (AI Team) employer: hyperexponential
As an Engineering Manager/Team Lead in our AI Team, you will thrive in a dynamic and innovative environment that champions collaboration and experimentation. Our culture prioritises psychological safety and high accountability, empowering you to lead a high-performing team while driving impactful AI solutions that enhance customer experiences. With a strong focus on professional growth and cutting-edge technology, we offer unique opportunities to shape the future of AI, making this an exceptional place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Engineering Manager/Team Lead (AI Team)
✨Tip Number 1
Network like a pro! Reach out to folks in your industry, especially those who work at companies you're eyeing. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Prepare for interviews by practising common questions and scenarios related to AI and engineering management. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 3
Showcase your projects! Bring examples of your past work, especially those that highlight your ability to lead teams and deliver complex AI solutions. Visuals or demos can really make an impact during discussions.
✨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, it shows you’re genuinely interested in being part of our innovative team.
We think you need these skills to ace Engineering Manager/Team Lead (AI Team)
Some tips for your application 🫡
Show Your Technical Leadership:When writing your application, make sure to highlight your experience in leading technical delivery. We want to see how you've tackled complex projects and turned ambiguity into successful outcomes. Share specific examples that demonstrate your ability to break down challenges and deliver results.
Emphasise Collaboration:Collaboration is key for us at StudySmarter. In your application, mention how you've worked closely with cross-functional teams like Product, Design, and Data Science. We love to see how you’ve shaped strategies and made trade-offs to achieve project goals.
Focus on Experimentation:We value a bias towards experimentation, so don’t shy away from discussing your experience with structured frameworks like A/B testing or spikes. Explain how these practices have helped you reduce risks and accelerate decision-making in your previous roles.
Tailor Your Application:Make sure to tailor your application to the role of Engineering Manager for our AI Team. Highlight your experience with AI/ML features and how you've improved engineering velocity through initiatives like CI/CD. Remember, applying through our website gives you the best chance to showcase your fit!
How to prepare for a job interview at hyperexponential
✨Understand the Technical Landscape
Before your interview, dive deep into the technical aspects of AI and ML systems. Familiarise yourself with concepts like CI/CD, testing infrastructure, and observability. Being able to discuss these topics confidently will show that you can lead the technical delivery of complex products.
✨Prepare for Collaboration Questions
Since this role involves close collaboration with cross-functional teams, think of examples where you've successfully partnered with Product, Design, or Research Engineering. Be ready to discuss how you navigated trade-offs and shaped roadmaps in previous projects.
✨Showcase Your Leadership Style
Reflect on your experience managing high-performing engineering teams. Prepare to share how you create psychological safety while maintaining high standards. Discuss specific instances where you coached team members to tackle complex problems and how you fostered a culture of accountability.
✨Emphasise Experimentation and Learning
This role values experimentation over theory, so come prepared with examples of how you've implemented structured experimentation frameworks. Talk about how you've used data and evaluations to guide decisions and improve outcomes, demonstrating your ability to turn uncertainty into actionable insights.
