Staff / Principal Machine Learning Engineer, Serving - USA

About the Position

Staff / Principal Machine Learning Engineer, Serving - USA

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

Location: Mountain View, California, USA

Base Salary Range: $270,000+ (bonus + equity + benefits)

Responsibilities

• Optimize AI models for real-time consumer applications.
• Collaborate with cross-functional teams to develop and deploy scalable AI solutions.
• Ensure the reliability and performance of AI models in production environments.

Requirements

• 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. Ability to profile code and optimize performance on NVIDIA GPUs.
• Distributed Systems & Scaling: Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and 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: Ability to 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.

We Offer

• Competitive salary
• Bonus
• Equity
• Benefits
• Relocation assistance

About the Company

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.

Apply now to join our team and help shape the future of real-time AI for consumer applications.