Job Description
About AssemblyAI
AssemblyAI builds the best-in-class Speech 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—from voice agents and meeting assistants to contact centers and medical scribes. Companies like Zoom, Granola, Fireflies, Cluely, and Calabrio rely on AssemblyAI to ship production-ready voice AI.
We’re at an inflection point in Speech AI. We released Universal-Streaming in mid-2025, and it has quickly earned its place as the model offering the best accuracy-latency-cost tradeoff on the market. Our research team drives these advances and ships with relentless velocity. Since releasing Universal-Streaming, we’ve already launched keyterms prompting feature and multilingual support—with more significant improvements on the roadmap.
We’ve raised $115M+ from Accel, Insight Partners, Y Combinator’s AI Fund, Patrick and John Collison, Nat Friedman, and Daniel Gross. We’re a remote team building one of the next great AI companies—and we’re looking for researchers who will shape its future.
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
Working at AssemblyAI
We are a small but mighty group of startup veterans and experienced AI researchers with over 20 years of expertise in Machine Learning, Speech Recognition, and NLP. As a fully remote team, we’re looking for people to join our team who are ambitious, curious, and lead with integrity. We’re still in the early days of AI and of AssemblyAI’s journey, and are looking for teammates who won’t just fit in, but will help us define and build our company culture.
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!
Using 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.
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Learn more about AI models for speech recognition
Speech-to-Text | Speech Understanding | LLM Gateway | Try the Playground
Our $50M Series C fundraise
