Mountain View, California, USA Staff / Principal Machine Learning Engineer, Serving - USA

Inworld, Inc.; Mountain View (CA); On-site; USD 270,000 - 500,000; Full time

Benefits offered by this job

Relocation assistance

Competitive compensation package

Job summary

Inworld, Inc. in Mountain View, CA is seeking a Staff/Principal ML Engineer to advance real‑time, scalable voice models.

You will drive inference optimization, model acceleration, and high‑performance backend systems, taking models from research through production with containerization and robust serving. A strong track record in distributed systems, production‑level code, and open source contributions is expected.

Qualifications

• Inference optimization expertise with scalable serving
• Experience with model acceleration techniques including quantization and distillation
• Proficiency in profiling and optimizing code for NVIDIA GPUs
• Distributed systems experience with Kubernetes, Ray and multi‑node inference
• End‑to‑end ownership from research to production deployment
• PhD or equivalent practical experience in backend or ML systems

Responsibilities

• Take a model from research to containerized production deployment
• Optimize serving latency and throughput while maintaining reliability
• Own full lifecycle for models in production and scale to thousands of concurrent connections

Skills

Inference optimization

Model acceleration

High‑performance systems

Distributed systems & scaling

Public work

Full‑cycle ownership

Background

Education

PhD in CS, Physics, Math or equivalent

Tools

C++

CUDA

Rust

Python

Kubernetes

Ray

Job description

Realtime TTS-2 is live, and we cut prices in half or more for most developers, across the whole stack. Try the live demo Read the TTS-2 launch See the cost reductions Staff / Principal Machine Learning Engineer, Serving - USA Mountain View, California, USA

About Inworld

Inworld is a research lab of top researchers and engineers, building the world’s top-ranked realtime voice models.

Today our models are the #1 ranked realtime voice models in the world. They are used to power the largest consumer-facing AI applications available, across categories like health, fitness, learning, therapy, companions, customer experience and media; representing 100s of millions of end users. Our work spans areas like research and development of state-of-the-art models, optimizing realtime inference, and creating best-in-class APIs and products that allow developers to engage their users.

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

• 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.