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Senior Researcher

📅 Sep 1
JAXTpusFlaxOptax
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📜 Description

  • Raise the team's experimental velocity for launching and evaluating experiments.
  • Maintain and evolve the JAX training framework for large-scale distributed training on TPU.
  • Investigate data quality issues and build tooling to improve model learning.
  • Analyze production model accuracy and build evaluation harnesses.
  • Translate research prototypes into production-ready systems.
  • Optimize production inference for speech language models using advanced techniques.

🛠️ Requirements

  • 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.
  • 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.
Full job description

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

You’ll join the team of researchers and engineers behind AssemblyAI’s foundational speech models. The team trains, fine-tunes, and analyzes these models, and builds the tooling to do that at scale. Your work will become the foundation for every customer around the world who builds Voice AI on our platform.

We’re passionate about Speech AI and the real-world products it makes possible, and we know the field moves fast. Keeping our models ahead means problems can’t stall at team boundaries, so this role is cross-functional by design. You’ll work closely with research, infrastructure, and production engineering. Your job isn’t to hand problems off between those teams. You’ll learn enough of each domain to follow a problem all the way to resolution.

What You’ll Do

  • Research and develop new methods that push the frontier of Speech AI.
  • Improve the data our models learn from: investigate quality issues, build tooling that surfaces them, and turn 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.
  • Partner with researchers and engineers to turn research prototypes into production-ready systems, modernizing model architectures and inference stacks along the way.
  • Find and fix performance bottlenecks across our training stack.

What You’ll Need

  • Hands-on experience training or adapting (e.g., fine-tuning) frontier speech or multimodal language models.
  • Discipline in measurement and analysis. You stay skeptical of results, whether they come from you or a coding agent, until they hold up. You treat an unexplained improvement as a problem and dig in until you understand it.
  • Appetite for the whole pipeline. You bring deep expertise in your core area, but when a customer issue traces back to a data problem or an evaluation blind spot, you want to chase it down yourself. The people who thrive here went deep in one area first, then kept expanding outward.
  • Enthusiasm for building and sharing tools. Working at the frontier means the tools you need often don’t exist yet. You enjoy building your own for performance analysis and data processing, and you share them so their value compounds across the team.
  • Self-direction. You find problems on your own, run investigations with a high degree of autonomy, and focus on the work with the highest impact.

Strong Python skills.

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
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Our $50M Series C fundraise
Check us out on YouTube!

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