Senior Machine Learning Engineer, Inference
Join Inworld AI as a Senior Machine Learning Engineer to optimize and run real-time AI inference systems in a collaborative environment.
Posted by employer 1 day ago
First seen on Joblaze 1 day ago
Last verified on the company career page 1 day ago
What you'll build
- Optimize model serving
- Containerize models
- Ensure reliable production performance
- Handle high-volume inference
- Design benchmarks or prototypes
Must have
- Deep understanding of inference optimization
- Experience with model acceleration techniques
- Proficiency in C++, CUDA, Rust, or optimized Python
- Experience with distributed systems and scaling
- PhD in CS, Physics, Math, or equivalent experience
Nice to have
- Public work on systems programming projects
- Open-source contributions to inference engines
AI in the day-to-day
We build first-party speech models and serve LLMs for consumer-facing applications.
Requirements
- Education
- PhD
Not disclosed in this posting: years of experience, visa sponsorship.
Benefits
Joblaze summary
In this role, the Senior Machine Learning Engineer focuses on optimizing and deploying high-performance inference systems for real-time AI applications. Key skills include proficiency in C++, CUDA, and experience with modern serving frameworks, as well as a strong understanding of model acceleration techniques. This position is ideal for individuals with a background in computer science or related fields who thrive in ambiguous environments and are eager to tackle complex engineering challenges. Inworld AI fosters a collaborative culture that emphasizes impact and innovation in AI technology.
Joblaze insights
- Listed yesterday — first seen on Joblaze October 23, 2026. Last confirmed on Inworld AI's careers page October 23, 2026.
- Salary band is above the typical range for AI/ML roles (median ~$190,000).
- Starts above 86% of 205 comparable senior ai/ml roles in United States that list Python we track (median $182,000 across 71 companies). See Python salary trends
- Python appears in 52.6% of 557 comparable senior ai/ml roles in United States; Ray appears in 1.3% of 557 comparable senior ai/ml roles in United States.
Quick facts
- Is the Senior Machine Learning Engineer, Inference role remote?
- No — this is an on-site role in Mountain View, California, United States.
- What's the salary range?
- Inworld AI lists $250,000–$300,000 for this role.
- Where is the role based?
- Inworld AI is hiring for this position in Mountain View, California, United States.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: C++, CUDA, Kubernetes, Python, Ray, Rust.
- What seniority level is this role?
- Inworld AI targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Senior Machine Learning Engineer, Inference role at Inworld AI.
From the original posting
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 $250,000 - $300,000+ bonus + equity + benefits.
Standard company text repeated across Inworld AI's postings is omitted here.