Simulation Synthetic Data Engineer

Quick Facts

• Role: Simulation Synthetic Data Engineer

Description

As a Simulation Synthetic Data Engineer, you will help develop next-generation simulation technology powering AI/ML efforts through synthetic data. You will build production tools and datasets used across the organization to improve dataset quality and model performance. You will extend simulation frameworks, prototype quickly, and scale implementations into reliable pipelines.

Responsibilities

• Build, enhance, and operate tools and pipelines for scenario creation, simulation execution, and generation of annotated 3D and multi-modal synthetic datasets.
• Design and implement prototypes, proofs-of-concept, and production systems that expand the simulation stack.
• Deliver validated datasets to internal customers using procedural systems, generative models, scene variation tools, and diverse data sources.
• Collaborate with engineers, animators, and technical artists to turn creative intent into efficient, scalable pipelines for generating, varying, organizing, and validating scenarios.
• Develop tools, APIs, and reusable components to help other teams generate, manage, and automate simulation workflows.
• Stay current with advances in simulation, rendering, generative models, VLMs, and synthetic data; introduce new approaches where valuable.

Requirements

• Master’s degree in Computer Science or related field, or equivalent practical experience.
• Strong programming skills in Python with solid software engineering fundamentals.
• Experience with 3D graphics or game engines (rendering, animation, scene graphs, or content pipelines).
• Familiarity with machine learning workflows, especially perception tasks, generative models, or VLMs.
• Ability to work collaboratively with cross-functional partners (engineers, artists, researchers) with strong communication skills.

Benefits

• Opportunity to work on highly ambitious, next-generation simulation technology that turns ideas into production tools and datasets for AI/ML.
• Chance to directly impact dataset quality and model performance across multiple product areas.
• Collaboration with engineers, technical artists, animators, and research teams to build scalable pipelines for synthetic data generation.