At a Glance
- Tasks: Design and run experiments to enhance our cutting-edge 3D foundation model.
- Company: Join SpAItial, a leader in AI and spatial computing innovation.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Diverse workplace welcoming all backgrounds and perspectives.
- Why this job: Make a real impact in the future of 3D content across various industries.
- Qualifications: Experience in ML frameworks and a background in Computer Science or related fields.
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 seeking a Machine Learning Research Engineer to design, test, and refine high-impact experiments for training our 3D foundation model. You’ll run targeted ablations, implement promising research ideas, and integrate your findings into production‑scale pipelines.
Responsibilities
- Design and run ablation experiments to improve model performance.
- Evaluate new research papers and implement them on our data.
- Incorporate experiment findings into our training pipelines.
- Collaborate with research and engineering teams to maintain strong baselines.
Key Qualifications
- Experience evaluating training runs at different scales (data size, format types, etc.).
- Ability to fine‑tune models with various settings (e.g., ablating different estimators for depth, normals).
- Skill in evaluating research papers and implementing them with our data.
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
- Bachelor’s, Master’s, or equivalent experience in Computer Science, Machine Learning, or a related field.
Preferred Qualifications
- Familiarity with 3D representation learning (e.g., NeRF, Gaussian Splatting).
- Procedural generation toolkit.
- Understanding of generative modeling (e.g., VAEs, GANs, transformers).
- Background in working with large‑scale data such as those used for GenAI training.
- 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 - ML employer: Spaitial Ltd.
At SpAItial, we pride ourselves on being at the forefront of AI and spatial computing innovation, offering our Research Engineers a dynamic work environment that fosters creativity and collaboration. Our commitment to employee growth is reflected in our supportive culture, where you will have the opportunity to engage with cutting-edge technologies and contribute to groundbreaking projects that redefine industries. Located in a vibrant tech hub, we provide a unique blend of professional development opportunities and a diverse workplace that values every voice.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer - ML
✨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 ML and 3D models. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common ML questions and be ready to discuss your past projects in detail. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our mission at SpAItial.
We think you need these skills to ace Research Engineer - ML
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with machine learning and 3D representation. We want to see how your skills align with our mission at SpAItial, so don’t be shy about showcasing relevant projects or research!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and spatial computing. We love seeing candidates who are genuinely excited about pushing boundaries in technology.
Showcase Your Technical Skills:Be specific about your proficiency in Python and ML frameworks like PyTorch or TensorFlow. If you've worked on large-scale data or have experience with generative modeling, make sure to mention that too. We’re keen to know what you can bring to the table!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at SpAItial!
How to prepare for a job interview at Spaitial Ltd.
✨Know Your ML Frameworks
Make sure you brush up on your Python skills and get comfortable with ML frameworks like PyTorch, TensorFlow, and JAX. Be ready to discuss how you've used these tools in past projects, especially in relation to training models and running experiments.
✨Familiarise Yourself with 3D Concepts
Since the role involves working with 3D foundation models, it’s crucial to understand concepts like NeRF and Gaussian Splatting. Dive into some recent research papers on these topics and be prepared to share your insights during the interview.
✨Prepare for Technical Questions
Expect technical questions that assess your ability to evaluate training runs and fine-tune models. Practice explaining your thought process when designing ablation experiments and how you would approach integrating findings into production pipelines.
✨Showcase Your Collaborative Spirit
Collaboration is key in this role, so think of examples where you've worked effectively with research and engineering teams. Highlight your communication skills and how you’ve contributed to maintaining strong baselines in previous projects.
