At a Glance
- Tasks: Build and ship AI features from model to user experience.
- Company: Join a cutting-edge AI company focused on real-world applications.
- Benefits: Competitive salary, flexible work options, and opportunities for growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Make AI work for users and impact the future of technology.
- Qualifications: Experience in machine learning and coding with a passion for problem-solving.
The predicted salary is between 60000 - 80000 £ per year.
About the Role
A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time. As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production. This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.
Focus
- Build and ship AI features end-to-end (model → system → user experience)
- Design and iterate on prompts, tools, memory, and agent workflows
- Turn raw model outputs into structured, reliable, and predictable behaviors
- Debug issues across the full stack (model, orchestration, infra, UX)
- Optimize for latency, cost, and production reliability
- Develop lightweight evaluation frameworks to measure real-world performance
- Work closely with product and engineering to translate ambiguous problems into working systems
Tech Stack
- Python
- PyTorch / JAX
- LLMs (OpenAI‑style APIs, LLaMA, Qwen, etc.)
- Inference / serving (e.g. vLLM)
- Vector DB
Ideal Experience
- Strong foundation in machine learning and modern neural network architectures.
- Hands‑on experience with training, fine‑tuning, or deploying ML models
- Ability to write clean, production‑quality code
- Comfort working across abstraction layers (model → infra → product)
- Strong problem‑solving skills in ambiguous, fast‑moving environments
- Bias toward shipping, iteration, and continuous improvement
Outcomes
- ML models in production meet expected accuracy, latency, and reliability targets.
- Production issues are identified quickly, debugged effectively, and root causes addressed.
- Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
- Collaborates effectively with engineers, product, and research teams to deliver reliable ML‑powered features.
- Iterations on models and systems are driven by real‑world signals and measurable improvements.
Applied AI Engineer employer: Bjak
At A1, we pride ourselves on fostering a dynamic and innovative work culture that empowers our Applied AI Engineers to take ownership of their projects from inception to deployment. Located in a vibrant tech hub, we offer competitive benefits, continuous learning opportunities, and a collaborative environment where creativity thrives. Join us to be part of a forward-thinking team dedicated to transforming AI into practical solutions that enhance user experiences.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning space, attend meetups, and join online communities. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, PyTorch, or any LLMs. This gives potential employers a taste of what you can do and how you turn theory into practice.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges and be ready to discuss your approach to debugging and optimising systems. Remember, they want to see how you think!
✨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 joining our team at StudySmarter.
We think you need these skills to ace Applied AI Engineer
Some tips for your application 🫡
Show Your Passion for AI:When writing your application, let us see your enthusiasm for AI and machine learning. Share any personal projects or experiences that highlight your skills and interest in the field. We love seeing candidates who are genuinely excited about what they do!
Tailor Your Application:Make sure to customise your application to fit the role of Applied AI Engineer. Highlight relevant experience with Python, PyTorch, or any other tech stack mentioned in the job description. This shows us that you’ve done your homework and understand what we’re looking for.
Be Clear and Concise:Keep your application straightforward and to the point. Use clear language to describe your experiences and skills. We appreciate when candidates can communicate complex ideas simply, especially since this role involves translating ambiguous problems into working systems.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it makes the whole process smoother for everyone involved.
How to prepare for a job interview at Bjak
✨Know Your Tech Stack
Familiarise yourself with the technologies mentioned in the job description, like Python, PyTorch, and LLMs. Be ready to discuss your hands-on experience with these tools and how you've used them to build or optimise AI systems.
✨Showcase Problem-Solving Skills
Prepare examples of how you've tackled ambiguous problems in fast-paced environments. Highlight specific instances where you debugged issues across different layers, from model to user experience, and how you ensured reliability in production.
✨Demonstrate Your Shipping Mindset
Emphasise your bias towards shipping and iteration. Share stories about projects where you turned model capabilities into real product behaviour, focusing on how you iterated based on user feedback and real-world performance.
✨Collaborate Effectively
Be ready to discuss how you've worked with cross-functional teams, including engineers and product managers. Highlight your ability to translate ambiguous problems into actionable tasks and how collaboration led to successful ML-powered features.
