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- Tasks: Lead the development of machine learning models for predicting material maps from images.
- Company: Enigma recruits top ML talent for innovative AI-Tech startups.
- Benefits: Enjoy a competitive salary, equity package, and a collaborative culture.
- Why this job: Shape the future of 3D content creation in a fast-paced, impactful environment.
- Qualifications: Solid research background in computer vision or generative modelling; proficiency in PyTorch required.
- Other info: Work with leading studios in film, games, and design while engaging with the academic community.
The predicted salary is between 54000 - 84000 £ per year.
Direct message the job poster from Enigma
We recruit ML Talent for AI-Tech Startups
Applied Researcher | Machine Learning | Computer Vision | Generative Models | PyTorch | PBR Rendering | Multi-view Learning | Photometric stereo
About the Project
We\’re developing a machine learning pipeline that predicts high-quality material maps (diffuse, roughness, specular, metallic, normals) directly from photogrammetry images, eliminating the need for flash photography and manual input. This work is supported by a unique dataset of physically accurate, real-world scans that allow us to push well beyond the limits of most artist-created datasets.
As part of this effort, we\’re seeking a machine learning researcher to help bring this \”image-to-material\” pipeline to life, transforming how digital textures are created at scale.
Your Role and Impact
As a Machine Learning Applied Researcher, you’ll lead the development of core models that power this technology and contribute to fast-paced, product-focused innovation.
You will:
- Design and prototype generative models to predict material maps from real-world input
- Work with architectures such as U-Net, transformers, and diffusion models
- Develop training strategies for consistent results across multiple image views
- Collaborate with a cross-functional team to productionize research models
Key Responsibilities
- Build and train models to predict physically based rendering (PBR) maps from image data
- Explore methods for multi-view learning and cross-view consistency
- Leverage pretrained vision-language models (e.g., CLIP, DINO) for semantic alignment
- Use our proprietary real-world dataset to improve model generalization and realism
- Evaluate model performance on visual fidelity, accuracy, and generalizability
- Support research infrastructure and design new experiments from real data
- Present findings internally and help guide R&D strategy
- Integrate semantic context into predictions to improve cross-surface performance
- Stay engaged with the academic community and help identify research partnerships
Requirements
- Solid research background in computer vision or generative modeling (publications or thesis a plus)
- Experience with multi-view learning, inverse rendering, or 3D-aware models
- Proficiency in PyTorch and clean, maintainable ML code
- Hands-on experience working with large-scale image datasets and training pipelines
- Fast-moving, experiment-driven mindset with strong execution skills
Nice to Have
- Familiarity with rendering pipelines or 3D graphics tools (e.g. Blender, glTF, EXR workflows)
- Experience with material estimation, photogrammetry, or UV workflows
- Understanding of self-supervised learning (e.g., CLIP, DINO)
- Previous work involving physically based rendering or material scanning
- Contributions to open-source ML tools or frameworks
What We Offer
- An opportunity to shape the future of 3D content creation
- Work with some of the world’s most innovative studios in film, games, and design
- A fast-moving environment with real impact, autonomy, and room for growth
- Collaborative culture across engineering, research, and creative teams
- Competitive salary and equity package
Applied Researcher | Machine Learning | Computer Vision | Generative Models | PyTorch | PBR Rendering | Multi-view Learning | Photometric stereo
Seniority level
-
Seniority level
Mid-Senior level
Employment type
-
Employment type
Full-time
Job function
-
Job function
Information Technology
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Industries
Technology, Information and Media
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Applied Researcher employer: Enigma
Contact Detail:
Enigma Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Researcher
✨Tip Number 1
Make sure to showcase your hands-on experience with large-scale image datasets and training pipelines. Highlight any specific projects where you've successfully implemented multi-view learning or generative models, as this will resonate well with the job requirements.
✨Tip Number 2
Engage with the academic community by attending relevant conferences or workshops. This not only helps you stay updated on the latest research but also provides networking opportunities that could lead to collaborations or referrals.
✨Tip Number 3
Familiarise yourself with the tools and technologies mentioned in the job description, such as PyTorch and rendering pipelines. Consider working on personal projects that utilise these tools to demonstrate your proficiency and passion for the field.
✨Tip Number 4
Reach out directly to the job poster via LinkedIn or other professional networks. A brief, thoughtful message expressing your interest in the role and highlighting your relevant skills can make a strong impression and increase your chances of getting noticed.
We think you need these skills to ace Applied Researcher
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, computer vision, and generative models. Include specific projects or research that demonstrate your proficiency with PyTorch and large-scale image datasets.
Craft a Compelling Cover Letter: In your cover letter, express your passion for the role and the impact you hope to make. Mention your familiarity with multi-view learning and any experience with rendering pipelines or 3D graphics tools, as these are key aspects of the job.
Showcase Your Research Background: If you have publications or a thesis related to computer vision or generative modelling, be sure to mention them. This will strengthen your application and show your commitment to the field.
Highlight Collaboration Skills: Since the role involves working with cross-functional teams, emphasise any previous collaborative projects. Discuss how you’ve successfully integrated feedback and worked alongside engineers and creatives to achieve common goals.
How to prepare for a job interview at Enigma
✨Showcase Your Research Background
Be prepared to discuss your previous research in computer vision or generative modelling. Highlight any publications or significant projects you've worked on, as this will demonstrate your expertise and commitment to the field.
✨Demonstrate Technical Proficiency
Make sure you can talk confidently about your experience with PyTorch and large-scale image datasets. Be ready to explain your approach to building and training models, as well as any specific techniques you've used in multi-view learning or inverse rendering.
✨Engage with the Project's Goals
Familiarise yourself with the company's project on predicting material maps from photogrammetry images. Show enthusiasm for how your skills can contribute to this innovative pipeline and be ready to discuss ideas on improving model generalisation and realism.
✨Prepare for Collaborative Discussions
Since the role involves working with cross-functional teams, think about examples of past collaborations. Be ready to discuss how you’ve integrated feedback from different stakeholders and how you can contribute to a collaborative culture within the company.
