Job description

Why AssemblyAI AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. We have a lab, and we also power our own models in our own proprietary inference environment. 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 the best talent we can find to join a rapidly scaling team that's defining the future of the industry. We are one of the most capital-efficient AI companies on the planet - and are rapidly approaching $1M ARR per employee. We sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. We have thousands of customers building with us including the top startups and world renown enterprises, and are serving at tremendous scale: we process over 2 million hours of audio daily and handle more than 1 million API calls every day. This is a rare growth-stage opportunity where the business is proven, the revenue is durable, and the growth trajectory is steep and getting steeper. We have a super flat culture and expect our employees' to be deeply intimate with the details at all levels. 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. About the role: We're hiring a Software Engineer to help turn cutting-edge AI research into products our customers rely on every day. You'll sit on a fast-paced engineering team between our research org and our Applied teams, taking new model capabilities from proof of concept to robust, well-documented APIs serving production traffic at scale. What You’ll Do: Design and ship customer-facing APIs that expose new model capabilities, owning them from prototype through launch and ongoing iteration. Scale inference infrastructure to support our 1M+ api users Partner with researchers to productionize new techniques. For example, turning a notebook or POC into something with the latency, reliability, and ergonomics customers expect. Work with our customers and Applied teams to understand how users are building with our products, and feed that back into product design and research direction. Contribute across the stack as needed: backend services, inference infrastructure, SDKs, internal tooling. What You’ll Need: Strong backend engineering experience, including building and supporting machine learning infrastructure and/or customer-facing APIs in production. Comfort operating with ambiguity. Research timelines and product timelines don't always line up, and you're energized rather than frustrated by that. Genuine curiosity about AI/ML. You don't need to be a researcher, but you should want to understand what the models are doing well enough to make good engineering decisions around them. Strong collaboration skills. You'll be working daily with researchers, applied engineers, and sometimes customers, people with very different contexts and priorities, and the role only works if you enjoy that. 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: $190,000 - $225,000 Using AI in your interview If you’re selected for an interview, please read our Candidate AI Guidance . It explains how you can use AI at each stage of our process, from your application and take-home to the interviews themselves. GDPR privacy notice If you’re applying from the EU or UK, please review our Job Applicant Privacy Notice before applying. Keep exploring AssemblyAI Transcribe: Speech-to-Text | Streaming Speech-to-Text Understand: Speech Understanding | LLM Gateway Interact: Voice Agent API | Dictation API Try our models in the Playground See how 50+ LLMs rank on real voice tasks: Voice Standard Bench Watch us on YouTube

Requirements

Must-haves

Why AssemblyAI
Design and ship customer-facing APIs that expose new model capabilities, owning them from prototype through launch and ongoing iteration.
Scale inference infrastructure to support our 1M+ api users
Partner with researchers to productionize new techniques. For example, turning a notebook or POC into something with the latency, reliability, and ergonomics customers expect.
Work with our customers and Applied teams to understand how users are building with our products, and feed that back into product design and research direction.
Contribute across the stack as needed: backend services, inference infrastructure, SDKs, internal tooling.
Strong backend engineering experience, including building and supporting machine learning infrastructure and/or customer-facing APIs in production.
Comfort operating with ambiguity. Research timelines and product timelines don't always line up, and you're energized rather than frustrated by that.
Genuine curiosity about AI/ML. You don't need to be a researcher, but you should want to understand what the models are doing well enough to make good engineering decisions around them.
Strong collaboration skills. You'll be working daily with researchers, applied engineers, and sometimes customers, people with very different contexts and priorities, and the role only works if you enjoy that.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapimachine learningmlgollm

Platform: greenhouse
Effort: quick