Chaos

Machine Learning Engineer (m/f/d)

Remote Machine Learning Engineer role with clear candidate location fit.

PostedJul 11, 2026

Eligible countries2 accepted countries

Seniority signalSenior

Work settingRemote

Accepted candidate locations

BulgariaGermany

AWS Azure GCP LLM Python REST TypeScript

Role overview

Machine Learning Engineer (m/f/d)

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Key Responsibilities

Design, develop, and optimize machine learning models in one or more solutions, including asset generation and capture, render enhancement, scene intelligence, agentic design workflows, and intuitive design interactions.
Investigate and bring techniques from a variety of AI research areas, such as diffusion, super-resolution, conditioned generation, plus neural and differentiable rendering, into artists’ hands.
Evaluate, integrate, and orchestrate off-the-shelf third-party foundation models to accelerate feature development and deployment.
Mentor other engineers and contribute to the growth of the team’s knowledge and expertise in machine learning.
Collaborate with cross-functional teams and our ML Product Manager to define the product requirements and scope of delivery of solutions to product teams.
Work closely with our MLOps Engineer to develop and maintain pipelines for distributed training, inference optimization/quantization/serving, experiment tracking, model versioning & validation, and deployment to the cloud (AWS/Azure/GCP).
Implement appropriate model evaluation tests, data curation processes, and apply dataset-rights awareness, and responsible AI/governance.
Stay updated and share knowledge on the latest developments in machine learning, generative AI, natural language processing, and 3D visualization, and implement cutting-edge techniques to enhance our solutions.
Ensure high-quality code and documentation, following best practices in software development and machine learning.

Required Experience

5+ years of experience in software development and at least 3 years of experience in developing machine learning models and deploying them in production environments.
Strong expertise in one or more of relevant fields, including: generative AI / foundation models, diffusion, NLP/LLMs, 3D computer graphics, geometry processing, asset generation, and scene understanding.
Proficiency in Python and machine learning frameworks such as PyTorch is required. Knowledge in other languages such as C++, C# and TypeScript, and MLOps systems such as MLFlow, RunPod, is encouraged.
Master’s/PhD in Computer Science, Machine Learning, or a related field (or demonstrable equivalent) strongly preferred.

Tools & Systems

Core (used daily): Python and PyTorch, with an experiment-tracking tool such as MLflow or Weights & Biases. Practical experience using AI-assisted development tools (e.g. Claude Code, Codex) across coding, CI, and testing.
Helpful: Foundation-model APIs (OpenAI, Claude); efficient/local LLM inference (llama.cpp, vLLM); a DCC/3D package (Blender, 3ds Max or Maya); and node-based diffusion pipelines (ComfyUI).
Nice to have: Our cloud and ML platform (GCP, GKE, Vertex AI); data versioning (DVC); inference optimization and serving (TensorRT, Triton); neural rendering and radiance fields (nerfstudio, gsplat); and differentiable rendering (Mitsuba).

Required Skills

Excellent problem-solving skills and ability to work independently as well as in a team.
Easily explaining complicated AI/ML concepts and technical trade-offs to product managers and business stakeholders in simple terms.
A genuine curiosity about AI research, knowing how to separate useful tools from trends.
Strong communication and collaboration skills, with the ability to guide and mentor team members.

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