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 passion for innovative research.
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 are seeking a Machine Learning Research Engineer to design, test, and refine high-impact experiments for training our 3D foundation model. You will 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.
- 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.
- 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 in London 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 evident through continuous learning opportunities and a culture that values diverse perspectives, making it an ideal place for those looking to make a meaningful impact in the tech industry. Located in a vibrant area, we provide unique advantages such as access to cutting-edge resources and a supportive community that encourages professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer - ML in London
✨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 is your chance to demonstrate what you can do beyond just a CV.
✨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.
✨Tip Number 4
Don’t forget to 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 at SpAItial.
We think you need these skills to ace Research Engineer - ML in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Research Engineer role. Highlight your experience with ML frameworks like PyTorch or TensorFlow, and don’t forget to mention any relevant projects or research papers you've worked on.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about 3D foundation models and how your background makes you a great fit for SpAItial. Be sure to mention any specific projects that relate to the job description.
Showcase Your Projects:If you've contributed to open-source projects or have relevant publications, make sure to include them in your application. This not only demonstrates your expertise but also shows your commitment to the field of machine learning.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you’re genuinely interested in joining our team at SpAItial!
How to prepare for a job interview at Spaitial Ltd.
✨Know Your ML Frameworks
Make sure you're well-versed in Python and the ML frameworks mentioned in the job description, like PyTorch, TensorFlow, and JAX. Brush up on your coding skills and be ready to discuss how you've used these tools in past projects.
✨Familiarise with 3D Representation Learning
Since the role involves working with 3D models, it’s crucial to understand concepts like NeRF and Gaussian Splatting. Prepare to talk about any relevant experience you have and how you can apply that knowledge to SpAItial's projects.
✨Prepare for Technical Questions
Expect questions about evaluating training runs and fine-tuning models. Be ready to explain your thought process and methodologies when running ablation experiments or implementing research papers. Practising common technical interview questions can really help.
✨Show Your Collaborative Spirit
Collaboration is key in this role, so think of examples where you've worked with research and engineering teams. Highlight your ability to maintain strong baselines and how you’ve integrated findings into production-scale pipelines in previous roles.
