Machine Learning Engineer

Chaos

Machine Learning Engineer developing and optimizing ML models for Chaos software solutions. Collaborating across teams to enhance creative tools and design processes in architecture and media.

Posted 7/10/2026full-timeRemote • 🇧🇬 BulgariaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing machine learning models, particularly in generative AI and 3D visualization. Proficient in Python and machine learning frameworks, with a strong focus on MLOps practices and collaboration across teams.

Highest-signal resume keywords

Machine Learning Model DevelopmentGenerative AI / Foundation ModelsPython ProgrammingMLOps Systems (MLFlow, RunPod)3D Computer Graphics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenerative AIDiffusion TechniquesNatural Language Processing (NLP)3D VisualizationModel EvaluationData CurationExperiment TrackingModel VersioningDeployment to Cloud

Soft Skills

MentoringCollaborationKnowledge Sharing

Tools & Technologies

PyTorchAWSAzureGCPMLFlowRunPod

Certifications & Qualifications

Master’s/PhD in Computer ScienceMachine Learning

Industry Keywords

Asset GenerationScene UnderstandingGeometry ProcessingIntuitive Design InteractionsResponsible AI

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorchTypeScript

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Hybrid or remote working options