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. 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 Machine Learning Engineer to accelerate our AI research-to-production pipeline. You’ll build and improve the infrastructure that enables our research team to rapidly deploy and safely test new models, while helping ensure our production inference systems remain efficient, scalable, and reliable. You’ll identify gaps and opportunities in our ML infrastructure, scope solutions to ambiguous technical problems, and help set the technical direction for how we bridge research innovation and production reliability. This role requires a strong backend engineering background in distributed systems and containerization, and a track record of independently driving projects from concept to delivery. This is a cross-functional role that requires close collaboration with both research teams developing models and engineering teams supporting the broader platform.
What You’ll Do:
- Design and implement tooling that enables researchers to quickly deploy and evaluate new models in production
- Design, build, and maintain high-performance, cost-efficient inference pipelines, making architectural decisions about scaling, reliability, and cost trade-offs
- Proactively identify and resolve infrastructure bottlenecks, proposing and scoping improvements to iteration speed and production reliability
- Develop and maintain user-facing APIs that interact with our ML systems
- Implement comprehensive observability solutions to monitor model performance and system health
- Troubleshoot and lead resolution of complex production issues across distributed systems, driving root-cause analysis and implementing preventive measures
- Set the direction for and continuously improve our MLOps practices, identifying the highest-impact opportunities to reduce friction between research and production.
- Collaborate closely with research and engineering teams to align on technical direction, and help onboard and mentor engineers on ML infrastructure best practices.
What You’ll Need:
- Strong backend engineering experience with Python
- Experience building and operating distributed, containerized applications, preferably on AWS
- Proficiency implementing observability solutions (monitoring, logging, alerting, tracing) for production systems
- Ability to design and implement resilient, scalable architectures
- Track record of independently scoping and delivering complex technical projects from problem identification through production deployment
- Comfort navigating ambiguity and making pragmatic technical decisions when requirements are unclear or evolving
An ideal candidate should also have some of the following:
- MLOps experience, including familiarity with PyTorch and Kubernetes
- Experience working in fast-paced environments where you owned technical direction for an area and drove projects with minimal oversight.
- Experience collaborating with remote, globally distributed teams
- Comfort working across the entire ML lifecycle from model serving to API development
- Experience in audio-related domains (ASR, TTS, or other domains involving audio processing)
- Experience with other cloud providers
- Familiarity with Bazel and monorepos
- Experience with alternative ML inference frameworks beyond PyTorch
- Experience with other programming languages
- Experience mentoring junior engineers or onboarding teammates onto complex systems
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: $195,000 - $225,000
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 GatewayTry the Playground Our $50M Series C fundraise Check us out on YouTube!