Job Summary
As a Staff Machine Learning engineer, you'll be a pillar of our engineering team, playing a pivotal role in designing, building, and deploying the next generation of AI powered solutions as well as MLOps processes and tooling.
- Minimum Qualification : Degree
- Experience Level : Mid level
- Experience Length : 5 years
Job Description/Requirements
Responsibilities
In this role, you’ll:
- Lead the design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability.
- Partner with Product Managers, Tech Leads and other stakeholders to analyze business problems and design solutions.
- Design and implement tools and processes to evaluate accuracy and performance of models.
- Help to define ML strategy for the organization, leveraging state-of-the-art Machine Learning, Deep Learning and Gen AI to address the business problems.
- Work closely with software engineers to incorporate ML solutions into software and provide a quality product for customers.
- Drive high engineering standards on the team; mentor and provide technical guidance to junior and mid-level engineers, promoting best practices and coding standards.
- Ensure a high operational standard to meet Twilio’s SLOs and Customers expectations.
Required:
- Strong background in the foundations of machine learning and building blocks of modern deep learning.
- 5+ years of applied ML experience with a track record of building, shipping and maintaining machine learning models in production.
- Proficiency in Python (preferred) or similar OO language.
- Experience utilizing Large (or Small) Language Models within software systems.
- A passion for machine learning and a deep knowledge of the growing AI ecosystem.
- Familiarity with MLOps concepts related to maintaining models in production such as testing, versioning, model registry, retraining, and monitoring.
- Excellent written and verbal communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.
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