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# Principal Machine Learning Infrastructure Engineer, Ads & Discovery New

- **Company:** Roblox
- **Location:** San Mateo, CA, United States
- **Experience:** Experienced
- **Salary:** $295,250.0 - $345,040.0
- **Contract:** Permanent contract
- **Skills:** 3d Models, Artificial Intelligence, Nvidia CUDA, Distributed Computing Environment, Machine Learning, Azure Machine Learning, Feature Engineering, Large Language Models, Information Technology, Low Latency, Machine Learning Operations, Data Pipelines
- **Published:** September 28, 2026
- **Apply:** https://www.gamesjobsdirect.com/job/roblox/principal-machine-learning-infrastructure-engineer-ads-discovery/360286

## About the Role

* 5+ years of experience designing, building, and deploying large-scale machine learning systems in production environments.
 * 3+ years of experience tech leading ML infrastructure engineers
 * Strong communication skills and a collaborative approach to problem solving.
 * BS, MS, or Ph.D. in Computer Science, Engineering, or equivalent experience.

## Description

* You will co-design models and systems, working at the intersection of model architecture and ML infrastructure, partnering closely with core modelers, data and AI infrastructure engineers, and product teams to push the boundaries of large-scale training and serving. Your work will span recommendation, search, and agentic applications, including large transformer architectures, LLMs, generative rankers, and efficient offline and online content-understanding systems.
 * You will investigate model, data, and systems tradeoffs end to end-from data pipelines and distributed training to low-latency inference and production serving. This includes designing efficient KV-cache strategies, applying pruning and quantization, optimizing GPU utilization and memory efficiency, and developing custom kernels where needed.
 * You are comfortable working across modern ML systems technologies such as FSDP, vLLM, SGLang, CUDA, distributed training frameworks, inference engines, and GPU kernels, while remaining tool-agnostic and focused on achieving step-function improvements in model quality, throughput, latency, reliability, and cost.
 * Lead strategic planning and roadmap execution of scalable production-ready ML systems including model training, data pipelines, feature engineering and model inference.
 * Own the architecture, establish engineering best practices of scalability, reliability, and cost-effectiveness of ML infrastructure (e.g., training, serving, feature).
 * Work closely with data scientists, ML engineers, platform teams, and product stakeholders to design, implement, and operate robust ML platforms that accelerate model development and deployment.
 * Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading-edge technologies and practices.


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