At a Glance
- Tasks: Lead AI research and engineering projects to enhance creative production tools.
- Company: Join MITO AI, a pioneering platform transforming the video production industry.
- Benefits: Competitive salary, equity, and remote work options across Europe.
- Other info: Be the first AI Research Engineer and shape the future of creative production.
- Why this job: Make a real impact in AI while collaborating with filmmakers and creators.
- Qualifications: PhD in AI or related field with strong engineering skills required.
The predicted salary is between 59400 - 72600 £ per year.
MITO AI is a collaborative, AI-native platform reinventing how films, commercials, and music videos are made. We are building the operating system for a $300B+ global video production industry shifting to AI-native workflows.
The Role:
This is MITO's first AI Research Engineer hire. You will have responsibility for both the idea and its implementation — investigating recent advances in AI, designing rigorous experiments, developing new approaches when existing methods fall short, and building the systems that bring successful results into the product.
The role combines real research with real engineering. You will prototype new approaches, evaluate whether they work, and ship the ones that do. You will work closely with MITO's VP of AI with meaningful autonomy and genuine input into what we work on — shaped by product priorities and by what we learn from the filmmakers and creators using MITO every day.
We welcome candidates at different career stages, from recent PhD graduates with strong engineering skills to experienced Research Engineers. We will adjust the scope and level of the role to match the right person.
Key Responsibilities:
- Research & Engineering Ownership
- Own research and engineering projects that improve the quality, capability, control, and reliability of MITO's AI systems.
- Develop and test new approaches, then turn successful results into working product improvements.
- Assess new models and methods against real creative needs and integrate the ones that provide clear value.
- Evaluation & Infrastructure
- Design experiments and benchmarks to understand system behaviour — and build the evaluation infrastructure, datasets, tests, and human-review processes that make AI behaviour measurable and failures reproducible.
- Use product behaviour, user feedback, and recurring failures to identify the most impactful areas for improvement.
- Build data and experimentation systems that support faster, more reliable iteration.
- Cross-Functional Collaboration
- Work with engineers, product teams, filmmakers, designers, and users to define success and deliver improvements.
- Communicate results clearly — in writing and in conversation — so that research findings translate into product decisions.
About You:
IMPORTANT: This is not a role for ML engineers who have run training jobs but haven't done research. We need someone who can design experiments, interpret results carefully, and know the difference between a result that holds and one that doesn't.
- Research foundation: A PhD in machine learning, artificial intelligence, computer vision, or a related field.
- Engineering depth: Strong ability to turn research ideas into working, scalable systems — not just notebooks.
- Experimental rigour: Evaluation design, ablations, error analysis, and careful interpretation of results are how you work, not afterthoughts.
- Production experience: You have shipped and operated AI or machine learning systems in production, not just in research settings.
- Technical fluency: Python and at least one of PyTorch or JAX. Good software engineering practices: testing, version control, reproducibility, maintainable code.
- Independence: You can drive a project from problem definition to deployed improvement, and you know when to ask for input.
The role spans several areas. We do not expect depth in all of them — but you should bring real depth in at least one, and the curiosity and ability to learn the others: video generation, video understanding, or computer vision; diffusion models, flow-matching models, or multimodal transformers; multimodal representation learning, cross-modal retrieval, or personalisation in generative AI systems; evaluating image, video, and audio outputs where quality depends on human judgement; fine-tuning, post-training, or training generative, multimodal, reward, or evaluation models; distributed training, model serving, or inference optimisation.
Also useful: experience building AI products or creative tools for filmmakers or designers; Java or TypeScript (our product stack).
Compensation & Perks:
- Work on hard AI problems that arise from real creative production — not synthetic benchmarks.
- Take ideas from experiment through to implementation and see how they perform with professional filmmakers using the product.
- Broad exposure to generative and multimodal models, methods, and tools at the frontier.
- Substantial ownership from day one — this is the first research hire, and what you build shapes the function.
- Competitive salary and equity.
- Remote from the UK or Europe — we have a strong presence in Madrid and welcome candidates based there.
How to Apply:
Send us your CV alongside examples of your research and engineering work: publications, research projects, open-source contributions, prototypes, or deployed systems. We want to understand what you have built and how you think — not just where you have worked.
MITO AI — www.mito.ai
Locations
Artificial Intelligence Researcher Engineer in Cheshire, Warrington employer: MITO AI
At MITO AI, we pride ourselves on being an exceptional employer, offering a dynamic and innovative work culture that fosters creativity and collaboration. Our remote-first approach allows you to work from anywhere in Europe while providing ample opportunities for professional growth and development in the cutting-edge field of AI research. Join us to be part of a team that values your contributions and supports your journey in shaping the future of generative AI systems.
StudySmarter Expert Advice🤫
We think this is how you could land Artificial Intelligence Researcher Engineer in Cheshire, Warrington
✨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 MITO AI 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 MITO AI.
✨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 MITO AI.
✨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 MITO AI 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 Artificial Intelligence Researcher Engineer in Cheshire, Warrington
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 MITO AI.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at MITO AI 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 MITO AI
✨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 MITO AI 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.