At a Glance
- Tasks: Own and optimise inference services and APIs for cutting-edge AI models.
- Company: Join SpAItial, a leader in generative AI and 3D environments.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic team environment with a commitment to diversity and inclusion.
- Why this job: Be at the forefront of AI innovation and make a real impact.
- Qualifications: 3+ years in software engineering with strong Python skills.
The predicted salary is between 63000 - 77000 £ per year.
SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments.
We’re seeking a Research Engineer to own the backend that serves our world models. You will build and run the inference systems behind our product and API, turning frontier research models into fast, reliable, and scalable services. This is a hands-on role for someone who has already owned production APIs in an AI system and can work closely with researchers to bring new models to users.
Responsibilities
- Own the inference services and APIs that power our product, from request to generated output.
- Optimize serving performance, reliability, and cost at scale.
- Maximize GPU utilization and minimize device memory footprint.
- Apply latest techniques for improving inference performance.
- Deploy research models as production services, including customer-specific configurations.
- Work with research and product colleagues to bring new models to users.
Key qualifications
- 3+ years of software engineering in an AI, machine learning, or computer vision product environment.
- Experience designing and owning production APIs.
- Strong Python, and experience shipping containerized services on GPU inference platforms.
- Hands-on GPU inference performance work: profiling, memory, batching, and cold start.
- Familiarity with 3D or computer vision data is a strong plus.
At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.
ML Inference Engineer employer: Spaitial Ltd.
At SpAItial, we pride ourselves on being an innovative employer that fosters a collaborative and dynamic work culture. As a Graphics Research Engineer, you will have the opportunity to work alongside a top-tier research team in a cutting-edge environment, with access to advanced tools and technologies. We are committed to employee growth, offering continuous learning opportunities and the chance to contribute to groundbreaking projects in AI and graphics.
StudySmarter Expert Advice🤫
We think this is how you could land ML Inference Engineer
✨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 Spaitial Ltd. 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 Spaitial Ltd..
✨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 Spaitial Ltd..
✨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 Spaitial Ltd. 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 ML Inference Engineer
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 Spaitial Ltd..
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Spaitial Ltd. 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 Spaitial Ltd.
✨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 Spaitial Ltd. 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.
