Senior Software Engineer, Inference - AssemblyAI

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 $500K 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.

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, 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 customers with different contexts and priorities.