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.
• Partner with researchers to productionize new techniques. For example, 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 We're Looking For:

• Strong backend engineering experience, including building and supporting 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 sometimes customers, people with very different contexts and priorities, and the role only works if you enjoy that.

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 reflects U.S. expectations. Outside of the U.S., ranges may vary and will be communicated during the process. Compensation is flexible based on experience and skill level.

Salary range: $200,000 - $225,000

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 Role

We're seeking an exceptional Senior Software Engineer to join our LLM team. This role is focused on building and maintaining our LLM gateway service—a unified API platform that connects customers to multiple LLM providers. You'll work on high-impact projects that directly solve customer problems, improve their AI and agentic workflows, and ensure reliable access to the best models for their use cases.

This is a deeply customer-focused role. You'll work closely with our customer success team to understand customer challenges, help optimize their prompt strategies, build features that address their pain points, and ensure our service reliably delivers value. We're looking for someone who combines software engineering excellence with genuine curiosity about how customers use AI and a drive to make their lives better.

As a Senior Engineer, you'll drive technical execution within the team, taking ownership of significant features and integrations while mentoring more junior engineers. You should be passionate about writing clean, maintainable code, implementing comprehensive testing strategies, and building highly reliable systems in service of solving real customer problems.

This role requires close collaboration with customer success, product managers, external API providers, and other engineering stakeholders. You'll need to balance technical excellence with pragmatic delivery in a fast-paced startup environment where uptime, reliability, and customer success are critical.

What You'll Do

Solve Customer Problems

• Partner closely with the customer success team to understand customer use cases, challenges, and integration needs
• Translate customer pain points surfaced by the CS team into technical solutions and product improvements
• Build features and tooling that directly address customer needs and improve their workflows
• Provide technical guidance and expertise to the customer success team to help them support customers effectively

Drive Technical Execution

• Own and deliver complete features and integrations within our LLM gateway service
• Build and maintain integrations with multiple LLM providers and AI services (OpenAI, Anthropic, Google Vertex, AWS Bedrock etc.)
• Write clean, maintainable, well-tested code following best practices
• Design and implement scalable, fault-tolerant solutions with appropriate abstractions
• Proactively identify and address technical debt, reliability issues, and code quality concerns
• Participate in on-call rotation to ensure service reliability and rapid incident response

Elevate Engineering Standards

• Conduct thorough code reviews focused on maintainability, testing, reliability, and architectural concerns
• Ensure proper test coverage across unit, integration, and end-to-end testing levels
• Improve code maintainability and extensibility through targeted refactoring
• Contribute to runbooks, incident postmortems, customer-facing documentation, and knowledge sharing
• Identify bottlenecks and propose solutions to improve team velocity and system reliability

Build and Collaborate

• Work closely with product, customer success, and engineering teams to deliver solutions
• Collaborate with external API providers to troubleshoot integration issues and improve reliability
• Translate ambiguous customer requirements into well-scoped technical work
• Proactively communicate status, risks, and blockers
• Mentor junior engineers on coding standards, reliability practices, and customer-centric thinking
• Participate in architectural discussions and contribute to technical decisions

What You'll Need

Core Requirements

• 5+ years of professional software engineering experience
• Strong proficiency in Go with demonstrated ability to write production-quality code
• Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) and understanding of prompt engineering concepts
• Experience building or working with API gateways, proxies, and rate limiting systems
• Deep experience with prompt engineering, chain-of-thought, few-shot learning, and other LLM optimization techniques
• Experience building applications with llms or ai agents with cascading architectures
• Solid understanding of software engineering fundamentals:
• Data structures and algorithms
• System design and architectural patterns
• Testing strategies (unit, integration, end-to-end)
• Code review practices and technical collaboration
• Experience with:
• RESTful APIs and distributed systems concepts
• Building or integrating with third-party APIs
• SQL and database design
• Version control (Git) and CI/CD pipelines
• Containerization (Docker) and cloud infrastructure
• Strong communication skills and ability to collaborate effectively with cross-functional teams
• Track record of delivering high-quality, reliable software in a fast-paced, team environment
• Ability to thrive in a startup environment with changing priorities and rapid iteration

Nice to Haves

• Experience with AWS services (ECS, Lambda, ElastiCache/Redis, RDS, S3, CloudWatch, API Gateway)
• Experience with monitoring and observability tools (Datadog, CloudWatch)
• Experience with request routing, load balancing, and traffic management
• Experience with circuit breakers, retry logic, fallback strategies, and fault tolerance patterns
• Understanding of API design, versioning, and backward compatibility
• Experience working closely with customer success or support teams

What We're Looking For

This role requires someone who is:

• Customer-obsessed - You genuinely care about solving customer problems and making their lives easier, working through the customer success team to understand and address their needs
• Excellent at software fundamentals - You write code that others want to emulate
• Quality-focused - You care deeply about testing, documentation, and maintainability
• Reliability-obsessed - You understand that uptime and performance directly impact customer trust
• LLM-savvy - You understand prompt engineering, model capabilities, and how to help customers get the best results
• Integration-savvy - You've wrestled with third-party APIs and know how to build resilient integrations
• Collaborative - You work well with diverse stakeholders and partner effectively with customer success teams
• Communicative - You can explain complex technical concepts clearly to teammates and cross-functional partners
• Growth-minded - You're curious, eager to learn, and want to expand into platform and infrastructure engineering
• Pragmatic - You balance perfection with delivery and understand trade-offs
• Team-oriented - You improve not just the code, but the team's overall effectiveness
• Proactive - You anticipate problems before they become incidents and build systems defensively
• Empathetic - You put yourself in the customer's shoes and design solutions accordingly

We're looking for the best person for this role - someone who can hit the ground running while growing with the team. The ideal candidate brings strong software engineering discipline, a passion for AI/LLMs, and genuine excitement about solving customer problems through reliable, thoughtfully designed infrastructure.

Pay Transparency:

AssemblyAI strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity across our team. Our salary ranges are set to be competitive for our size, stage, and industry, and reflect just one component of the full compensation, benefits, and rewards we offer.

Salary determinations consider a variety of factors, including relevant experience, technical depth, skills demonstrated during the interview process, and maintaining internal equity with peers on the team. The range shared below represents a general expectation for the posted position. However, we are open to considering candidates who may fall above or below the outlined experience level—in those cases, we will communicate any adjustments to the expected salary range.

The range provided applies to candidates located in the United States. For candidates outside of the U.S., compensation ranges may differ; any adjustments will be communicated throughout the interview process.

Salary range: $180,000 - $240,000 

The expected base compensation for this role is listed above. Our total compensation package includes competitive equity grants, 100% employer-paid benefits, and the flexibility of being fully remote. 401k match up to 4% for US-based full time team members.