Software Engineer - Machine Learning
To see similar active jobs please follow this link: Remote Development jobs
About the role:
We're looking for a Machine Learning Engineer to accelerate our AI research-to-production pipeline. This person will build infrastructure enabling our research team to rapidly deploy and safely test new models while maintaining efficient, scalable production inference systems. This person should have a strong backend engineering background in distributed systems and containerization, and be deeply interested in optimizing the path from research innovation to production value. 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
Build and maintain high-performance, cost-efficient inference pipelines in production
Optimize infrastructure for both 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 complex production issues across distributed systems
Continuously improve our MLOps practices to reduce friction between research and production
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) for production systems
Ability to design and implement resilient, scalable architectures
An ideal candidate should also have some of the following:
MLOps experience, including familiarity with PyTorch and Kubernetes
Experience working in startup environments demonstrating ownership, decisiveness, and rapid iteration
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 Ray.io, Bazel, and monorepos
Experience with alternative ML inference frameworks beyond PyTorch
Experience optimizing for low-latency, real-time inference
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: $157,500-$175,000
About the job
Software Engineer - Machine Learning
To see similar active jobs please follow this link: Remote Development jobs
About the role:
We're looking for a Machine Learning Engineer to accelerate our AI research-to-production pipeline. This person will build infrastructure enabling our research team to rapidly deploy and safely test new models while maintaining efficient, scalable production inference systems. This person should have a strong backend engineering background in distributed systems and containerization, and be deeply interested in optimizing the path from research innovation to production value. 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
Build and maintain high-performance, cost-efficient inference pipelines in production
Optimize infrastructure for both 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 complex production issues across distributed systems
Continuously improve our MLOps practices to reduce friction between research and production
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) for production systems
Ability to design and implement resilient, scalable architectures
An ideal candidate should also have some of the following:
MLOps experience, including familiarity with PyTorch and Kubernetes
Experience working in startup environments demonstrating ownership, decisiveness, and rapid iteration
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 Ray.io, Bazel, and monorepos
Experience with alternative ML inference frameworks beyond PyTorch
Experience optimizing for low-latency, real-time inference
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: $157,500-$175,000