Inworld Ai · Remote · Mountain View, California, USA
About Inworld
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.
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.
Who We're Looking For
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.
Experience We Find Useful
You don't need all of this. But you need enough to make a case.
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.
Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
Who Thrives Here
You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
We believe in the power of in-person collaboration to solve the hardest problems and foster a strong team culture. We offer relocation assistance and look forward to you joining us in our Mountain View office.
The base salary range for this full-time position is $270,000 - $500,000+ bonus + equity + benefits.
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