Staff Machine Learning Engineer
Staff ML Engineer on Twilio's AI/ML Platform team builds the company-wide MLOps/LLMOps foundation: LLM inference and orchestration, model fine-tuning/distillation, and evaluation tooling to ensure quality across Twilio's AI features. Core stack is Python and cloud platforms (AWS/GCP/Azure), remote within Ontario, BC, or Alberta, Canada.
- Compensation
- CA$160.3K–CA$200.4K / yr
- Location
- Remote - Ontario, Canada (DNU)
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 8+ years applied ML/AI experience owning technically ambitious systems end to end
- Comfort operating in a 0-to-1 setting with ambiguous requirements and strong bias for action
- Proficiency in Python
- Experience building cloud-based services on AWS, GCP, or Azure with high-volume data, streaming or real-time inference, and various data stores
- Strong fundamentals of statistical ML algorithms, transformer models, and LLMs
- Extensive ML Ops and LLM Ops experience, including evaluation metrics for non-deterministic AI features at scale
- Strong communication and collaboration skills
- Familiarity with AI-assisted development tools (e.g., Claude, Codex, GitHub Copilot, Cursor)
Nice to have
- Prior experience building evals for autonomous agents and LLMs
- Prior experience building autonomous agents with multi-turn conversations and long-term context/consistency
- Prior experience working in a globally distributed team
- Evidence of technical leadership such as open-source contributions, published papers, blog posts, or talks
Full description
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.
Qualifications
Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!
*Required:
- 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.