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Why Join Inworld

Inworld is the leading provider of AI technology for real-time interactive experiences, with a $500 million valuation and backing from top tier investors including Intel Capital, Microsoft’s M12 fund, Lightspeed Venture Partners, Section 32, BITKRAFT Ventures, Kleiner Perkins, Founders Fund, and First Spark Ventures.

Inworld provides the market’s best framework for building production ready interactive experiences, coupled with dedicated services to optimize specific stages of development – from design and development, to ML pipeline optimization and custom compute infrastructure. We help developers bring their AI engines in-house with a framework optimized for real-time data ingestion, low latency, and massive scale. Inworld powers experiences built by Ubisoft, NVIDIA, Niantic, NetEase Games and LG, among others, and has partnerships with key industry players such as Microsoft Xbox, Epic Games, and Unity. 

Inworld was recognized by CB Insights as one of the 100 most promising AI companies in the world in 2024 and was named among LinkedIn's Top Startups of 2024 in the USA.

About the role

At Inworld, we’re building the AI framework behind the next generation of real-time, immersive applications. As a Staff MLOps Engineer, you’ll design, build and scale the infrastructure that powers intelligent AI agents across massive consumer experiences while ensuring performance, reliability, and speed at every level. 

What you’ll do

• Build and scale MLOps systems to streamline the end-to-end ML model lifecycle on the Inworld AI platform, from training to deployment.
• Design and implement robust model training, evaluation, and release pipelines.
• Collaborate cross-functionally with ML and backend teams to design, deploy, and maintain scalable secure infrastructure for Inworld’s AI Engine and Studio.
• Facilitate a "you build it, you run it" culture by providing the necessary tools and processes for monitoring the reliability, availability, and performance of services.
• Manage CI/CD pipelines to ensure smooth and efficient code integration and deployment.
• Identify and implement opportunities to enhance engineering speed and efficiency.
• Provide technical leadership in ML engineering best practices, raise the technical bar, and mentor junior engineers in MLOps principles.

Expected experience

• 7+ years of software engineering experience, with 5+ years of infrastructure-as-code
• Proficiency in managing Kubernetes clusters and applications, including creating Helm charts/Kustomize manifests for new applications.
• Experience in creating and maintaining CI/CD pipelines for both applications and infrastructure deployments (using tools like Terraform/Terragrunt, ArgoCD, GitHub Actions, Ansible, etc.).
• Deep knowledge of at least one major cloud provider (Google Cloud Platform, Microsoft Azure, Oracle Cloud).
• Proficient in at least one backend programming/scripting languages such as Golang, Python, and Bash.
• Knowledge of SLURM or similar job schedulers for distributed training.
• Experience with data pipeline and workflow management tools
• Familiarity with open source LLM and open source serving solution (e.g. vLLM or llama.cpp, kserve, etc) is a plus.
• Experience with bare metal GPUs (optional).
• Desire to work at a fast-growing Series A startup, comfortable with uncertainty, owning and scaling new products, and embracing an experimental and iterative development process.

In-office location: Mountain View, CA, United States. You must be available for hybrid work. 

The US base salary range for this full-time position is $180,000 - $280,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future.

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