Why AssemblyAIAssemblyAI 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.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. 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.We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed.About the RoleWe'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.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.The role is embedded within the Research team.What You’ll DoRaise 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 NeedExpert-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.BonusDomain 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.Salary range: $270,000 - $310,000AI 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.
6 days ago