Head of Machine Learning Engineering

About the Role

Twilio is looking for a leader, innovator, and team builder in applied Machine Learning who will invent new ways for Machine Learning to improve and power the next generation of communication products. You will lead teams in natural language processing, speech recognition, dialog management, and general machine learning with a focus on high availability, scale, and product impact. You bring deep experience in machine learning and complex distributed systems, and are comfortable with both the team and engineering requirements involved in bringing AI systems to production. You have an in-depth understanding of the algorithmic side of machine learning, proven experience building delivering production quality models, and demonstrated experience building and managing productive teams to drive machine learning impact in organizations. You have familiarity with ML and NLP tools (TensorFlow, spaCy, scikit-learn), you drive the architecture of the systems, and you are comfortable interfacing with data pipelines and data stores such as MySQL, DynamoDB, and Kafka. You engage with the broader machine learning community, and understand how advances to the state-of-the-art can power products.

What You'll Do

As a Head of Machine Learning Engineering, you will be leading and growing a talented team of machine learning engineers and distributed systems engineers building Twilio's next generation machine learning products. You will directly lead ML Platform as well as evangelize the application of ML within Twilio across our product lines. In doing so, you will create, own and operate machine learning platforms, as well as the REST APIs, microservices and data pipelines that help us power products with machine learning. You will be responsible for growing and developing the engineering team.

Requirements

• 10 years of people management / leadership experience with a track-record of hiring and developing high performance teams.
• MS in a ML field including but not exclusive to Natural Language Processing, Speech Recognition or equivalent experience.
• Deep experience in ML / DL design & implementation -- not only at the algorithm level, but also at the system architecture level.
• Proven ability to build high impact machine learning products and teams that positively impact the bottom line.
• Proven experience in taking machine learning models to production environments.
• Demonstrated experience building complex distributed systems with high reliability and scalability.
• Demonstrated success scaling production backend systems. You can design and develop horizontally scalable, resilient and performing-under-load systems.