At a Glance
- Tasks: Engineer cutting-edge AI systems and develop decision-making loops for intelligent tools.
- Company: Innovative tech firm leading the charge in AI development.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on innovation and collaboration.
- Why this job: Join a team pushing the boundaries of AI and make a real impact.
- Qualifications: 5+ years in ML, strong coding skills, and experience with production models.
The predicted salary is between 48000 - 72000 £ per year.
We are building the next generation of intelligence. This isn’t just about calling an API; it’s about engineering the plumbing, the memory, and the reasoning logic that allows AI to navigate complex datasets. You will be responsible for delivering fast, brilliant, and architecturally sound solutions.
What You’ll Own:
- Cognitive Architecture: Beyond simple prompts, you will engineer the decision‑making loops (agents) that allow our tools to self‑correct and execute multi‑step coding tasks.
- Context Engineering: Develop the retrieval and embedding logic that ensures the model “sees” the right data at the right time, minimizing noise and maximizing signal.
- System Integrity: Move beyond “vibe‑based” testing. You’ll build rigorous, automated frameworks to quantify model behaviour and prevent regressions in production.
- Model Lifecycle: Own the decision between fine‑tuning a specialised small model versus orchestrating a frontier LLM, balancing latency with reasoning depth.
- Technical Leadership: Act as the “Engineer’s Engineer,” setting the standard for how we write production‑grade ML code and mentor the team on high‑stakes delivery.
Your Technical Toolkit:
- The GenAI Stack: Extensive experience with the “Agentic” ecosystem (orchestration frameworks, vector‑native databases, and semantic search).
- Production ML: A history of shipping models that actually handle traffic. You know that “done” means deployed, monitored, and stable.
- Code‑Fluent: You are a strong software engineer. You are as comfortable in the depths of a Python backend as you are tweaking a model’s temperature. Familiarity with JVM‑based languages (Java/Kotlin) is a significant edge.
- The Scientific Method: You don’t guess; you experiment. You have a background in statistical validation and know how to prove a model’s value via data.
Why You’re a Fit:
- You find the “unknowns” of Agentic AI exciting, not paralyzing.
- You believe that a model is only as good as the data pipeline feeding it.
- You are tired of “wrapper” apps and want to build deep, integrated AI systems.
- You have 5+ years of total ML experience, with a heavy recent focus on the LLM frontier.
Senior Machine Learning Systems Engineer employer: iForce Connect
Join a forward-thinking company in London that is at the forefront of AI innovation, where your expertise as a Senior Machine Learning Systems Engineer will be valued and nurtured. With a collaborative work culture that encourages experimentation and technical leadership, you will have ample opportunities for professional growth while contributing to groundbreaking projects. Enjoy competitive benefits and the unique advantage of working in a vibrant city known for its tech scene, making this an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Systems Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 related to machine learning systems. This is your chance to demonstrate your technical prowess and problem-solving abilities in action.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice coding challenges and be ready to discuss your past experiences in detail. We want to see how you think and approach problems!
✨Tip Number 4
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 and contributing to the next generation of intelligence.
We think you need these skills to ace Senior Machine Learning Systems Engineer
Some tips for your application 🫡
Show Your Passion for AI:When writing your application, let your enthusiasm for AI and machine learning shine through. We want to see that you’re not just ticking boxes but genuinely excited about building the next generation of intelligence.
Tailor Your Experience:Make sure to highlight your relevant experience in cognitive architecture and model lifecycle management. Use specific examples from your past work that demonstrate how you've tackled similar challenges to those we face at StudySmarter.
Be Clear and Concise:While we love detail, clarity is key! Make your application easy to read by using straightforward language and breaking up large blocks of text. This helps us quickly grasp your skills and experiences.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves.
How to prepare for a job interview at iForce Connect
✨Know Your Tech Inside Out
Make sure you’re well-versed in the GenAI stack and can discuss your experience with orchestration frameworks and vector-native databases. Be ready to share specific examples of how you've built or improved ML systems, as this will show your technical depth.
✨Demonstrate Problem-Solving Skills
Prepare to talk about how you approach complex datasets and decision-making loops. Think of a few scenarios where you’ve had to troubleshoot or innovate under pressure, and be ready to explain your thought process clearly.
✨Showcase Your Leadership Experience
As a Senior Machine Learning Systems Engineer, you’ll need to mentor others. Be prepared to discuss times when you’ve led a project or guided a team, focusing on how you set standards for production-grade ML code and ensured high-stakes delivery.
✨Emphasise Your Experimental Mindset
Highlight your familiarity with the scientific method and statistical validation. Share examples of how you’ve used data to prove a model’s value, and be ready to discuss any experiments you’ve conducted to refine your models.
