Senior Software Engineer - Machine Learning
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
About the job
Apply for this position
Senior Software Engineer - Machine Learning
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