Staff Machine Learning Engineer

Who we are

At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences.

Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands.

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Hiring and how we work

We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions!

Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings.

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See yourself at Twilio

Join the team as Twilio’s next Staff Machine Learning Engineer on Twilio’s AI/ML Platform team.

About the job

We are actively recruiting for this role to fill an existing vacancy. This position is needed to build the foundational AI/ML capabilities and common platform tooling that power intelligent engagement experiences across Twilio. You will influence the design and evolution of our company-wide ML/AI Ops foundation, ensuring every AI capability incorporates rigorous offline and online evals to maintain exceptionally high quality.

You will drive improvements to managing LLM workloads across the company, while ensuring easy and compliant access to data for AI/ML exploration and product development. This role requires a builder mindset to bridge theoretical research, production-grade experimentation, and scalable, enterprise-quality software.

Responsibilities

In this role, you’ll:

Conduct deep-dive research into and implement state-of-the-art LLM inference and orchestration.

Identify opportunities to leverage small language via fine-tuning/distillation.

Build ML systems that hold up at Twilio scale.

Set standards and tooling for building evals across a range of AI/ML capabilities.

Rapidly acquire new technical skills and knowledge in a fast-paced, high-delivery environment.

Proactively leverage and integrate the latest AI development stacks to bypass boilerplate engineering and focus on core differentiation.