Staff Machine Learning Engineer

Hiring and how we work

See yourself at Twilio

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

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.

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.
• 8+ years applied ML/AI experience with a track record of owning technically ambitious systems end to end rather than contributing to someone else's design.
• Comfort operating in a 0-to-1 setting with ambiguous requirements and a strong bias for action.
• Proficiency in Python programming language.
• Experience building cloud-based services on AWS, GCP, or Azure, with high-volume data, streaming or real-time inference paths, and a range of data stores.
• Strong fundamentals of statistical machine learning algorithms, transformer models, LLMs.
• Extensive experience with ML Ops and LLM Ops patterns, including designing and implementing rigorous evaluation metrics for non-deterministic AI features at scale.
• Strong communication skills and highly effective collaborator who works well with teammates.
• Familiarity with AI-assisted development tools (e.g., Claude, Codex, GitHub Copilot, Cursor, etc.).
• Prior experience building evals for autonomous agents and large language models.
• Prior experience building autonomous agents that handle multi-turn conversations and maintain long-term context/consistency.
• Prior experience working in a globally distributed team.
• Evidence of technical leadership/impact, such as contributions to open-source data/ML projects and/or published technical presentations, blog posts, papers, or research.

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