# Staff Machine Learning Engineer -  Computer Vision & Multi-Modal AI

> Jobs in XR — Extended reality talent marketplace

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Unity3d is hiring. Negotiable · Full Time · Human.

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## Summary

| Field | Value |
| --- | --- |
| Company | Unity3d |
| Budget | Negotiable |
| Type | Full Time |
| Worker | Human |
| Posted | 2026-07-05 |
| Apply | https://www.jobsinxr.com/jobs/unity3d_staff-machine-learning-engineer-computer-vision-and-multi-modal-ai_6944578b |
| Company page | https://www.jobsinxr.com/companies/unity3d |

## Description

The opportunity
We are building the next generation of AI-driven game experiences — generative world models, neural rendering, and multi-modal understanding that turn images, text, and 3D primitives into interactive worlds. As our Staff Machine Learning Engineer, you will be a core technical leader bringing state-of-the-art computer vision and multi-modal models — transformers, diffusion networks, vision-language models (VLMs), and JEPA-style architectures — from research into robust, production-grade systems.
This is a deeply hands-on, high-impact role. You will help define the modeling and deployment strategy, drive architectural decisions across the ML stack, and mentor a team of senior and mid-level engineers. Your work will directly shape the quality, capability, and performance of AI features experienced by billions of players — across cloud, server, and on-device targets.
What you'll be doing
Technical Leadership
- Help set the technical vision and roadmap for computer vision and multi-modal AI models, spanning transformers, diffusion models, vision-language models, and JEPA-style generative architectures.
- Drive design and implementation of models for image and video understanding, generation, segmentation, detection, and dense prediction, as well as multi-modal reasoning over images, text, and 3D inputs.
- Make sound decisions on model architecture, training strategy, data pipelines, and evaluation — balancing quality, capability, latency, and cost across deployment targets.
- Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving, with deployment spanning cloud GPUs through to efficient on-device inference where the product requires it.
Architecture & Research Translation
- Collaborate directly with research scientists to translate novel CV and multi-modal model architectures into deployable, well-engineered implementations.
- Design scalable systems for multi-modal inference that process diverse inputs images,
- video, text, primitives, and metadata — and produce rich outputs from semantic
- predictions to pixel-level generation.
- Track and rapidly adopt breakthroughs across the field: vision-language pretraining and
- alignment, efficient diffusion (e.g., consistency models, flow matching), efficient attention
- e.g., FlashAttention, linear-attention variants), and tokenization/representation learning
- for vision.
- Where latency or device constraints demand it, apply compression, quantization, pruning, and knowledge distillation, and work with appropriate runtimes (e.g., TensorRT, ONNX Runtime, CoreML, TFLite) to meet performance budgets.
- Team & Cross-Functional Leadership
- Lead and mentor a team of ML engineers; define engineering best practices, code review standards, and rigorous benchmarking and evaluation methodology.
- Partner with research, platform engineers, product managers, and runtime teams to align ML capabilities with product roadmaps and target-platform constraints.
- Champion a culture of measurement: define KPIs for model quality, accuracy, latency, memory, and cost, and ensure the team tracks them rigorously.
What we're looking for
- 6+ years in ML engineering, with significant depth in computer vision and/or multi-modal modeling.
- Proven production experience with transformer-based and diffusion-based v

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_Generated 2026-07-15 for Jobs in XR._