At a Glance
- Tasks: Build and deploy AI systems that solve real-world problems in a fast-paced environment.
- Company: Join a dynamic YC-backed tech start-up transforming traditional industries with AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Enjoy significant ownership and collaboration in a rapidly growing team.
- Why this job: Make a tangible impact by turning complex challenges into innovative AI solutions.
- Qualifications: 2+ years in Data Science or AI Engineering with strong Python skills.
The predicted salary is between 49500 - 60500 £ per year.
We’re working with a fast-growing YC-backed technology company using AI to transform a complex, traditionally manual industry. Already working with major customers and backed by leading investors, they’re growing their engineering team and looking for an Applied AI Engineer to build production AI systems used across critical real-world workflows.
The Role
This is an applied, product-focused AI role rather than a research position. You’ll take real operational problems and turn them into production AI/ML systems, working across experimentation, modelling, evaluation, deployment and iteration. You’ll work closely with engineers, domain experts and users, with significant ownership over what gets built and how it performs in production.
What You’ll Do
- Build and deploy production AI/ML systems solving real customer and operational problems
- Develop, evaluate and iterate on models and AI-driven workflows
- Turn complex, ambiguous problems into scalable technical solutions
- Work closely with users and domain experts to understand problems and shape the product
- Own projects from initial experimentation through to production
- Help shape AI engineering practices as the team scales
Requirements
- 2+ years' experience across Data Science, Machine Learning or AI Engineering
- Experience taking ML/AI systems beyond experimentation and into production
- Strong Python and practical ML fundamentals
- A product mindset and a track record of building solutions around real user problems
- Comfortable working across modelling, engineering and deployment
- Able to communicate effectively with technical and non-technical stakeholders
- Comfortable operating with high ownership in a fast-moving start-up environment
Applied AI Engineer (AI/ML) - London - YC-backed start-up employer: Stott and May
Join a dynamic and innovative YC-backed start-up in London, where you'll have the opportunity to make a significant impact by transforming traditional industries with cutting-edge AI technology. The company fosters a collaborative work culture that encourages creativity and ownership, providing ample opportunities for professional growth and development. With a focus on real-world applications and a supportive team environment, this role offers a unique chance to contribute to meaningful projects while working alongside industry experts.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI Engineer (AI/ML) - London - YC-backed start-up
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Stott and May!
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We think you need these skills to ace Applied AI Engineer (AI/ML) - London - YC-backed start-up
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Stott and May, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Stott and May. 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 Stott and May
✨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!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨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 Stott and May!
✨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.
