Staff / Principal Machine Learning Engineer, Inference

📋 Job Overview

Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.

🏢 About Inworld

Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.

We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.

🎯 The Role

A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.

✅ Key Responsibilities

• Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
• Model Acceleration. Hands-on experience with quantization, distillation, caching strategies, continuous batching, paged attention, and speculative decoding.
• High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
• Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
• Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
• Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.

🎁 Benefits

The base salary range for this full-time position is $270,000 - $500,000+ bonus + equity + benefits.

🛂 Visa & Eligibility

We offer relocation assistance and look forward to you joining us in our Mountain View office.