In this role, you'll:

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
• Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor, test, and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply.

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In this role, you'll:

Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads. Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling. Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets. Monitor, test, and improve data quality, model performance, latency, and cost. Partner with product, data science, and security teams to ship resilient, compliant services. Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices. Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions. Embrace Twilio's 'We are Builders' values by taking ownership of problems and driving them to completion.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply.

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This role offers a rare opportunity to make a meaningful impact at the intersection of AI and national security. You will help build Generative AI data-labeling pipelines from the ground up, create operational processes to manage and optimize an in-house expert data workforce, and develop novel technology-driven approaches (e.g., scripts, prompt engineering, hybrid data) to improve the quality of our training and evaluation datasets.

Responsibilities:

• Develop, build, and maintain the infrastructure required to ensure data pipelines are efficient, scalable, and produce high-quality outputs
• Take ownership of day-to-day progress on high-priority data production pipelines, ensuring projects move forward efficiently
• Partner with subject matter experts in their fields to validate the quality of our data and to translate deep domain knowledge into scalable processes and measurable outcomes
• Work closely with customers to understand their requirements and design data taxonomies that optimize model performance
• Utilize analytics and data visualization tools to track progress, identify bottlenecks, and make data-driven decisions to optimize pipeline performance
• Influence cross-org collaboration to define and advance human data strategy, influencing technical and non-technical stakeholders to ensure data quality, scalability, and long-term platform leverage
• Own larger and larger components of our data delivery processes, until you ultimately serve as the full owner of our most visible and high impact customer pipelines

You have:

• 5+ years of experience in product development, data science, or operations
• A history of successful project management and comfort in ambiguity
• Ability to analyze complex operational data, build queries, and identify trends to inform decisions and optimize processes
• Technical aptitude to understand how to produce data for state of the art post-training techniques such as supervised fine tuning (SFT), reinforcement learning through human feedback (RLHF), Reinforcement Learning with Verifiable Rewards (RLVR) etc

Nice to have:

• Experience working in defense tech and/or an AI company
• A technical degree in fields like computer science, data science, or engineering
• A deep understanding of ML operations for generative AI workflows / products
• An active Top Secret security clearance

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

The base salary range for this full-time position in the location of Washington DC is: $169,600-$212,000 USD

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