Asks for

The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“Expert-level proficiency with JAX and TPUs, including the surrounding ecosystem (Flax, Optax, the XLA compilation pipeline).”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“Expert-level proficiency with JAX and TPUs, including the surrounding ecosystem (Flax, Optax, the XLA compilation pipeline).”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“Expert-level proficiency with JAX and TPUs, including the surrounding ecosystem (Flax, Optax, the XLA compilation pipeline).”
The ad says“Investigate and resolve performance bottlenecks across the stack, from low-level kernels (XLA, Pallas) to high-level system design.”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“Strong Python skills; C++ or Rust experience for kernel-level work is a plus.”
The ad says“Familiarity with modern inference optimization techniques: continuous batching, KV-cache management, sharding strategies, quantization.”
The ad says“Optimize production inference for speech language models, both from a serving architecture perspective and through advanced techniques such as quantization and speculative decoding.”
The ad says“The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.”
The ad says“Expert-level proficiency with JAX and TPUs, including the surrounding ecosystem (Flax, Optax, the XLA compilation pipeline).”
The ad says“Expert-level proficiency with JAX and TPUs, including the surrounding ecosystem (Flax, Optax, the XLA compilation pipeline).”

Why AssemblyAI

AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences. The Voice AI space is at an inflection point; we’re looking for folks truly excited to join a small team and help define the future of the industry.

We are one of the most capital-efficient AI companies on the planet - with under 100 people generating roughly $600K ARR per employee, we sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. That's not an accident; it's a deliberate choice to stay lean, move fast, and give every person on the team outsized ownership and impact. With thousands of customers including Granola, Fireflies, Figure AI, and CallRail, the company has real scale - processing over 2 million hours of audio daily and handling more than 1 million API calls every day. This is a rare growth-stage opportunity where the business is proven and the trajectory is steep, but the team is still small enough that your fingerprints are on everything.

If you've ever felt buried under layers of bureaucracy, starved of real ownership, or frustrated watching your work disappear into a slow-moving org, AssemblyAI is built differently. The company operates as a true meritocracy, with no heavy planning or approval processes and no gatekeeping on the tools or information you need. For anyone who genuinely cares about voice AI, not as a trend to chase, but as a technology to build, this is the place where the most interesting problems at the most interesting scale are being solved by a team small enough that you'll actually know everyone's name.

We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. No matter your race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply!

About the Role

We're looking for a Senior Research Engineer to join our Research team, developing and improving the systems behind large-scale distributed training, data processing, and inference. Our goal as an organization is to solve customer problems and improve our products quickly through model development and measurement — and how fast we move depends on how quickly anyone here can run an experiment, measure it, and find out what's wrong. Raising that ceiling is the heart of this role. You'll be working inside the pipeline you're improving, not alongside it.

The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques.

This is a cross-functional role. You'll work closely with our researchers, our infrastructure team, and production engineering — not as a handoff point, but as the person who learns enough of each domain to follow problems through to resolution. At times you'll train models, run evaluations, and analyze data yourself, both to deliver impact directly and to learn what's worth building to multiply the team's work. The bar is someone who understands the end-to-end impact they intend to make, measures it from the outset, and would rather find out they were wrong in a week than in a quarter. That discipline is what turns cross-functional ownership into an advantage.

The role is embedded within the Research team.

What You’ll Do

Raise the team's experimental velocity — make it faster to launch an experiment job, get a number back you can trust, and know what to try next.
Maintain and evolve our JAX training framework, keeping it scalable and efficient for large-scale distributed training runs on TPU.
Improve the data our models learn from: investigating quality issues, building the tooling to surface them, and turning what you find into measurable accuracy gains.
Analyze the accuracy of production models, build evaluation harnesses, and work out which improvements will matter most to customers.
Translate research prototypes into production-ready systems, refactoring and modernizing model architectures and infrastructure along the way.
Optimize production inference for speech language models, both from a serving architecture perspective and through advanced techniques such as quantization and speculative decoding.
Investigate and resolve performance bottlenecks across the stack, from low-level kernels (XLA, Pallas) to high-level system design.
Partner with researchers, infrastructure, and production engineering to trace problems to their real source and ship fixes that hold.

What You’ll Need

Expert-level proficiency with JAX and TPUs, including the surrounding ecosystem (Flax, Optax, the XLA compilation pipeline).
Measurement discipline. You define what success looks like before you start, you stay skeptical of your own results until they hold up, and you treat an unexplained improvement as a problem rather than a win.
Appetite for the whole pipeline. Your core strength might be JAX and TPU performance, but when a customer issue traces back to a data problem or an evaluation blind spot, you want to go find it yourself. The people who do well here went deep in one area first, then kept expanding outward.
Strong experience optimizing inference systems for production, ideally with LLMs or speech models.
Deep understanding of distributed training at scale, modern deep learning systems, and ML infrastructure best practices.
Familiarity with modern inference optimization techniques: continuous batching, KV-cache management, sharding strategies, quantization.
Enthusiasm for refactoring and improving existing systems — you thrive on making products and code faster and better.
Strong Python skills; C++ or Rust experience for kernel-level work is a plus.
Excellent communication and a collaborative mindset — you can clearly explain complex tradeoffs and prioritize high-impact work.

Bonus

Domain knowledge in Speech-to-Text: ASR architectures, audio processing, streaming inference.

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on paying competitively for our size, stage, and industry, and are one part of many compensation, benefit, and other reward opportunities we provide.

There are many factors that go into salary determinations, including relevant experience, skill level, qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

The provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range which will be communicated to candidates throughout the interview process.

Salary range: $270,000 - $310,000

AI to Interview:

If you’re selected for an interview, please review this resource to better understand how AssemblyAI approaches the use of AI in our interview process.

GDPR privacy notice:

Candidates from the EU should review this job applicant privacy notice before applying.

Keep Exploring AssemblyAI:

Speech-to-text | Streaming speech-to-text | Speech Understanding | LLM Gateway
Try the Playground
Our $50M Series C fundraise
Check us out on YouTube!