Staff Machine Learning Engineer @ Twilio

Job Information

Job Description:

Responsibilities
In this role, you’ll:

Build and maintain scalable machine learning solutions in production
Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness
Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems
Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements and define the scope of the systems needed
Work closely with data platform teams to build robust scalable batch and realtime data pipelines
Work closely with software engineers, build tools to enhance productivity and to ship and maintain ML models
Drive high engineering standards on the team through mentoring and knowledge sharing
Drive engineering best practices around code reviews, automated testing and monitoring

Not all applicants will have skills that match a job description exactly. Twilio values diverse experiences in other industries, and we encourage everyone who meets the required qualifications to apply. While having “desired” qualifications make for a strong candidate, we encourage applicants with alternative experiences to also 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:

7+ years of applied ML experience.
Proficiency in Python is preferred. We will also consider strong quantitative candidates with a background in other programming languages
Strong background in the foundations of machine learning and building blocks of modern deep learning
Track record of building, shipping and maintaining machine learning models in production in an ambiguous and fast paced environment.
Track record of designing and architecting large scale experiments and analysis to inform product roadmap.
You have a clear understanding of frameworks like – PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do
Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring.
Demonstrated ability to ramp up, understand, and operate effectively in new application / business domains.
You’ve explored modern data storage, messaging, and processing tools (Kafka, Apache Spark, Hadoop, Presto, DynamoDB etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar
Experience working in an agile team environment with changing priorities
Experience of working on AWS

Desired:

Benefits:
Experience Level: Senior
Work From: Onsite