At a Glance
- Tasks: Create data pipelines and evaluate cutting-edge 3D models in a dynamic AI environment.
- Company: Join SpAItial, a pioneering startup redefining 3D content generation.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Collaborate with visionaries in a fast-paced, innovative setting.
- Why this job: Be at the forefront of the AI revolution and shape the future of 3D technology.
- Qualifications: Degree in applied machine learning and experience with modern 3D techniques.
The predicted salary is between 36000 - 60000 € per year.
SpAItial is pioneering the development of a frontier 3D foundation model, pushing the boundaries of AI, computer vision, and spatial computing. Our mission is to redefine how industries, from robotics and AR/VR to gaming and movies, generate and interact with 3D content. We’re looking for individuals who are bold, innovative, and driven by a passion for pushing the boundaries of what’s possible. You should thrive in an environment where creativity meets challenge and be fearless in tackling complex problems. Our team is built on a foundation of dedication and a shared commitment to excellence, so we value people who take immense pride in their work and place the collective goals of the team above personal ambition. As a part of our startup, you’ll be at the forefront of the AI revolution in 3D technology, and we want you to be excited about shaping the future of this dynamic field. If you’re ready to make an impact, embrace the unknown, and collaborate with a talented group of visionaries, we want to hear from you.
Responsibilities
- Create data processing pipelines for large-scale training data for training foundation 3D models.
- Prototype, implement, and evaluate architectures, losses, and features to complex generative models.
- Design, monitor and analyze experiments of large training runs of foundation models.
- Building pipelines for data filtering, annotation, and curation.
- Building evaluation and visualization frameworks to provide insights on model performance.
- Close collaboration with researchers to provide data for training large models.
Key Qualifications
- University degree focusing on applied machine learning.
- Experience in modern 3D techniques (NeRFs, Gaussian Splatting) or image and video generative models (Stable Diffusion, VAEs, etc).
- Proficiency in Python and deep learning frameworks (PyTorch).
- Familiarity with cloud-based ML infrastructure (e.g., AWS, GCP, or Azure).
- Strong problem-solving skills and the ability to work independently in a fast-paced environment.
Preferred Qualifications
- Industry experience in ML.
- Deep understanding of generative modeling, including VAEs, GANs, and transformers.
- Experience in 3D geometry processing (Structure-from-Motion, SLAM, depth prediction, …).
- Background in working with large-scale data and training runs.
- Experience with multi-modal LLMs (e.g., for image/video captioning).
- Contributions to open-source generative AI projects or relevant publications.
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.
Research Engineer - Productionization employer: Spaitial Ltd.
At SpAItial, we pride ourselves on being an exceptional employer that fosters innovation and creativity in the rapidly evolving field of 3D technology. Our collaborative work culture encourages bold thinkers to tackle complex challenges while providing ample opportunities for professional growth and development. Located at the heart of a vibrant tech community, we offer a unique chance to be part of a pioneering team that is shaping the future of AI and spatial computing.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer - Productionization
✨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 put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to 3D models and generative AI. This is your chance to demonstrate your creativity and problem-solving abilities, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions and be ready to discuss your past experiences in detail. We want to see how you tackle challenges head-on!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are genuinely excited about joining our mission at SpAItial.
We think you need these skills to ace Research Engineer - Productionization
Some tips for your application 🫡
Show Your Passion:When you're writing your application, let your enthusiasm for AI and 3D technology shine through. We want to see that you're not just qualified, but genuinely excited about pushing the boundaries of what's possible in this field.
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your relevant experience with modern 3D techniques and machine learning. We love seeing how your unique skills align with our mission at SpAItial, so don’t hold back!
Be Clear and Concise:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see why you’re a great fit for the Research Engineer role.
Apply Through Our Website:We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Spaitial Ltd.
✨Know Your 3D Stuff
Make sure you brush up on modern 3D techniques like NeRFs and Gaussian Splatting. Be ready to discuss how you've applied these in your past work or projects, as well as any experience with generative models like VAEs or GANs. Showing that you’re not just familiar but passionate about these technologies will definitely impress.
✨Show Off Your Problem-Solving Skills
SpAItial is all about tackling complex challenges, so be prepared to share specific examples of how you've approached tough problems in the past. Think about times when you had to innovate or adapt quickly—this will demonstrate your ability to thrive in a fast-paced environment.
✨Get Technical with Python and ML Frameworks
Since proficiency in Python and deep learning frameworks like PyTorch is key, make sure you can talk about your experience with them confidently. You might even want to prepare a mini-project or example that showcases your skills, especially if it relates to data processing pipelines or model evaluation.
✨Collaborate Like a Pro
Collaboration is crucial at SpAItial, so think about how you’ve worked with others in the past, especially researchers or cross-functional teams. Be ready to discuss how you’ve contributed to team goals and how you value collective success over personal ambition. This will show that you fit right into their team-oriented culture.
