Machine Learning Engineer

Job Location and Work Mode

This position is based in Bulgaria, offering flexibility with hybrid or fully remote working arrangements. Candidates must possess a valid work or residence permit applicable for Bulgaria.

Company Overview

Chaos is a globally recognized software company that delivers innovative visualization and design technologies to support creative professionals. Operating for more than two decades, Chaos serves diverse sectors including architecture, design, media, and e-commerce. Its products empower architects, designers, VFX artists, and animators to collaborate, streamline workflows, and create immersive experiences. The company's headquarters are in Karlsruhe, Germany, with offices in 11 locations worldwide. The recent merger with Enscape and acquisition of companies like Cylindo, AXYZ Design, and Evolve Lab have expanded Chaos' expertise and product offering.

Role Summary

The Machine Learning Engineer will spearhead the creation and integration of intelligent, ML-driven solutions across the Chaos software suite, including Enscape, V-Ray, and Veras. Key application areas include image enhancement, automated assistance, asset creation, rendering improvements, and interactive 3D design interfaces. This role bridges state-of-the-art machine learning research with practical software deployment.

Primary Responsibilities

• Design, develop, and enhance machine learning models targeting asset generation, rendering optimization, scene intelligence, agentic workflows, and user-friendly design interaction.
• Research and integrate AI innovations such as diffusion models, super-resolution techniques, conditioned generation, and neural/differentiable rendering into products for artist use.
• Evaluate and implement third-party foundational AI models to expedite feature innovation and release.
• Provide mentorship to fellow engineers, contributing to collective ML expertise growth.
• Collaborate with cross-disciplinary teams and the ML Product Manager to set product requirements and delivery scope.
• Coordinate with MLOps specialists to maintain pipelines supporting distributed training, inference optimization (quantization and serving), experiment tracking, model version control, validation, and cloud deployment on AWS/Azure/GCP.
• Implement model evaluation frameworks, data curation policies, and promote responsible AI governance and dataset rights awareness.
• Stay current on advances in machine learning, generative AI, NLP, and 3D visualization, applying these insights to enhance products.
• Ensure excellent code quality and documentation aligned with best practices in software and ML development.

Candidate Requirements

• A minimum of 5 years software development experience, including at least 3 years engaged in machine learning model creation and production deployment.
• Expertise in areas such as generative AI, foundation models, diffusion, NLP/large language models, 3D graphics, geometry processing, asset generation, or scene understanding.
• Strong proficiency in Python and ML frameworks like PyTorch; familiarity with C++, C#, TypeScript, and MLOps tools (MLFlow, RunPod) is advantageous.
• Master's or PhD in Computer Science, Machine Learning, or related fields preferred, or equivalent proven experience.

Technical Tools and Systems

• Daily use: Python, PyTorch, machine learning experiment tracking tools (MLflow, Weights & Biases), and AI-assisted development tools (e.g. Claude Code, Codex) for coding, continuous integration, and testing.
• Beneficial but optional: APIs for foundation models (OpenAI, Claude), efficient local LLM inference tools (llama.cpp, vLLM), digital content creation software like Blender, 3ds Max or Maya, and node-based diffusion pipeline tools (ComfyUI).
• Preferred experience: cloud ML platforms (GCP, GKE, Vertex AI), data versioning (DVC), inference serving and optimization technologies (TensorRT, Triton), neural rendering frameworks (nerfstudio, gsplat), and differentiable rendering tools (Mitsuba).

Personal and Professional Skills

• Excellent analytical and problem-solving abilities with capacity for independent and collaborative work.
• Ability to clearly communicate complex AI/ML topics and trade-offs to both technical and non-technical stakeholders.
• Keen interest in AI research with discernment to distinguish enduring tools from passing trends.
• Strong interpersonal and mentorship capabilities to support team development.

Additional Notes

The company encourages candidates committed to teamwork, reliability, and continuous learning. Applications must be submitted in English. Only shortlisted candidates will be contacted, and confidentiality of all applications is assured.