At a Glance
- Tasks: Build and evolve AI systems that power investment insights for professional investors.
- Company: Dynamic fintech company focused on real market impact and innovation.
- Benefits: Competitive salary, equity options, and a collaborative in-office environment.
- Other info: Enjoy real ownership, technical autonomy, and support from senior peers.
- Why this job: Directly influence investor decisions and tackle challenging AI problems.
- Qualifications: 5+ years in ML or AI systems, strong Python skills, and startup experience.
The predicted salary is between 90000 - 160000 £ per year.
You have built real AI systems that ship, break, and get fixed under pressure. You care less about model demos and more about decisions that move capital. You want your work used daily by professional investors, not buried in notebooks. You are comfortable owning hard problems end to end. If you want a calm job optimizing benchmarks, this is not it.
The Role, In Plain English
You will build and evolve the AI systems that power Reflexivity's investment insights. This role exists because off-the-shelf models and generic pipelines are not enough. You will work at the intersection of reasoning engines, proprietary data, and real market impact. Your work directly affects how investors understand earnings, risk, and market catalysts.
What You'll Be Responsible For
- Design, fine-tune, and deploy ML and LLM-driven systems used in production by professional investors
- Build and maintain inference pipelines that are fast, observable, and reliable
- Integrate OpenAI, Gemini, and Anthropic models into reasoning and knowledge systems
- Work closely with backend engineers to productionize models in Golang-based services
- Improve signal quality, not just model accuracy
- Review code and designs with a bias toward long-term maintainability
What 'Good' Looks Like in This Role
After 3 months: You understand the product, data flows, and investor use cases deeply. You ship meaningful improvements.
After 6 months: You own major parts of the AI stack. Your work improves insight quality and latency measurably.
After 12 months: You are a technical reference point for AI decisions. You raise the bar for how AI is built at Reflexivity.
Who You Are (Must-Haves)
- 5 plus years building ML or applied AI systems in production
- Startup experience working on a core product, not a side project
- Strong Python skills and experience integrating with backend systems
- Hands-on experience with AI-assisted coding tools like Cursor or Claude Code
- Fintech experience or strong personal investment background
- Comfortable owning outcomes, not just tasks
Nice-to-Haves (Not Deal Breakers)
- Experience with LLM reasoning systems or knowledge graphs
- Exposure to Golang-based ML integrations
- Prior experience supporting investor-facing products
How We Work
- In-office team with high trust and high ownership
- Direct communication, minimal process, strong opinions backed by data
- Engineers are expected to think about product impact, not just code
- We move fast when it matters and slow down when correctness matters more
Why This Role Is Worth Your Time
- Direct influence on how professional investors make decisions
- Hard problems at the edge of AI, data, and finance
- Real ownership and technical autonomy
- Senior peers who care about quality and outcomes
Compensation & Practicalities
Base salary: £90,000 to £160,000 depending on experience. Equity included. In-office role based in London. No agency candidates.
Machine Learning and AI Engineer - London employer: Reflexivity
Reflexivity is an exceptional employer for Backend Software Engineers, offering a dynamic work environment in London where innovation meets finance. With a strong emphasis on technical ownership and a high-performance culture, employees are empowered to make impactful decisions that drive investor outcomes. The company fosters growth through mentorship and collaboration with AI and product teams, ensuring that every engineer has the opportunity to influence architecture and enhance their skills in a supportive, trust-based atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning and AI Engineer - London
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We think you need these skills to ace Machine Learning and AI Engineer - London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Reflexivity. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Reflexivity
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Reflexivity!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
