At a Glance
- Tasks: Lead the development of an AI SRE agent, transforming alerts into actionable insights.
- Company: Join Tracer, a pioneering startup revolutionising AI for production engineering.
- Benefits: Competitive salary, equity options, 30 days leave, and health insurance.
- Other info: Enjoy a dynamic work culture with team dinners, offsites, and growth opportunities.
- Why this job: Be at the forefront of AI innovation and make a significant impact in tech.
- Qualifications: 5+ years in software engineering with strong backend and distributed systems experience.
The predicted salary is between 70000 - 130000 £ per year.
About Us
Tracer is an early‑stage, venture‑backed startup building the open‑source AI SRE agent for production engineering.
We are backed by experienced operators and investors who believe AI for production systems will be one of the defining enterprise software categories of the next decade.
Our team is small, senior, and highly execution‑focused.
About the Role
Tracer is building an open‑source AI SRE agent ( that automatically investigates production incidents across the entire production stack.
We’re hiring a Lead AI Software Engineer in London to own the core architecture and ship an agent that produces grounded RCAs and fix suggestions across production infrastructure.
Humans stay in control of production decisions; the agent does the heavy lifting.
This is a hands‑on, high‑ownership role at the center of the product.
- Tech Stack
- Python + Lang Graph (for multi‑agentic alert investigation)
- Rust
- Click House (high‑volume event + investigation history at scale)
- AWS + Terraform (infrastructure that builds itself)
- Next. js + Type Script
- What You’ll Do
You’ll own the core systems that turn an alert into a defensible investigation and RCA. In practice, you will:
- Architect and build the core alert, investigation, root cause analysis (RCA) pipeline in Python.
• Design and implement key systems including
- Alert ingestion + normalization
- Context enrichment + correlation
- Problem framing outputs
- Hypothesis orchestration engine
- Investigation execution runtime
- Investigation artifacts + reporting
- Drive core architecture decisions and ensure the system is observable, auditable, and reliable from day one.
- Partner with founders to ship a small set of high‑value alert types that work extremely well, then expand coverage deliberately.
- Build customer‑ready integrations across the pipeline stack.
- Educate and guide future engineers, setting a high bar for technical quality, speed, and pragmatism.
- What We’re Looking For
- 5+ years (ideally 10+) professional software engineering experience.
- Proven track record of shipping real products at high velocity.
- Strong backend and distributed‑systems foundations, ideally with experience in data platforms and production pipeline stacks and incident/observability tooling.
- Experience working at an early‑stage startup (bonus for having joined earlier).
- High ownership and sharp product instincts: you build what matters and cut what doesn’t.
Compensation & Benefits
Total Compensation Range: £70,000 – £130,000+ (salary and equity value).
We structure compensation as follows
- Competitive base salary
- Meaningful equity ownership with real upside
- Final package depends on experience, impact, and seniority
What’s included
- Salary + equity (equity typically ~0.3% – 2%+)
- 30 days annual leave
- Employee health insurance
- Visa sponsorship Weekly team dinners and socials
- Regular team offsites and trips
- #J-18808-Ljbffr
Lead AI Software Engineer in London employer: Doist
Brink's is an exceptional employer that fosters a collaborative and innovative work culture, empowering employees to drive global commercial strategies in the ATM lifecycle solutions sector. With a strong focus on professional development and growth opportunities, employees are encouraged to expand their skills while contributing to sustainable growth and operational excellence. Located in a dynamic environment, Brink's offers unique advantages such as a diverse team and the chance to build long-term partnerships with global customers, making it a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Lead AI Software Engineer in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Doist or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Doist.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Doist.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Doist that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Lead AI Software Engineer in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Doist.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Doist and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Doist
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Doist uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
