# Principal Machine Learning & Data Engineer

**Company**: Twilio
**Location**: Remote - US
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $184,500 - $230,700
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/twilio/jobs/7155492?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_73de331f-92f

## Description

Join Twilio as a Principal Machine Learning & Data Engineer to lead the design, build, and operation of the internal ML-and-data platform that powers every customer interaction.

Responsibilities:

- Architect and evolve Twilio's end-to-end ML and real-time data platforms for reliability, security, and cost efficiency.

- Design scalable feature stores, streaming and batch pipelines, and low-latency model-serving layers on AWS.

- Implement MLOps best practices,automated testing, CI/CD, monitoring, and rollback,for hundreds of daily deployments.

- Own system design reviews, threat modeling, and performance tuning for high-volume communications workloads.

- Lead cross-functional engineering efforts, breaking down complex initiatives into executable roadmaps.

- Mentor staff and senior engineers, raising the technical bar through code reviews and pair programming.

- Partner with Product, Security, and Compliance to meet stringent privacy and governance requirements (HIPAA, SOC 2, GDPR).

- Champion a culture of experimentation, data-driven decision-making, and continuous improvement.

Qualifications:

- Bachelor's or higher in Computer Science, Engineering, Mathematics, or equivalent practical experience.

- 7+ years building and operating production data or machine-learning systems at scale.

- Expert fluency in Python and one compiled language (Java, Scala, Go, or C++).

- Hands-on mastery of distributed data frameworks (Spark/Flink), SQL/NoSQL stores, and streaming platforms (Kafka/Kinesis).

- Demonstrated success designing cloud-native architectures on AWS, including Terraform-managed infrastructure.

- Deep knowledge of container orchestration (Kubernetes/EKS), service-mesh networking, and autoscaling strategies.

- Practical experience implementing MLOps tooling such as MLflow, Kubeflow, SageMaker, or Vertex AI.

- Strong grasp of model-lifecycle concerns,feature engineering, offline/online parity, A/B testing, drift detection, and retraining.

- Proven ability to lead technical projects end-to-end and influence without authority across multiple teams.

- Exceptional written and verbal communication skills, with a bias toward clarity and action.

Location: This role will be remote, but is not eligible to be hired in CA, CT, NJ, NY, PA, WA.

Compensation: The estimated pay ranges for this role are as follows:

- Based in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $184,500 - $230,700.

- Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $195,300 - $244,200.

- Based in the San Francisco Bay area, California: $217,000 - $271,300.

## Skills

### Required
- Python
- Java
- Scala
- Go
- C++
- Spark
- Flink
- Kafka
- Kinesis
- AWS
- Terraform
- Kubernetes
- EKS
- MLflow
- Kubeflow
- SageMaker
- Vertex AI

### Nice to have
- Graduate degree focused on machine learning, distributed systems, or applied statistics
- Contributions to open-source ML or data infrastructure projects
- Experience with privacy-enhancing technologies (differential privacy, homomorphic encryption) or on-device inference
- Background in conversational AI, real-time communications, or large-language-model deployment at scale
- Exposure to compliance-heavy environments (HIPAA, PCI-DSS) and secure multi-tenant design patterns
- Published research, patents, or conference talks in ML systems or data engineering

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/twilio/jobs/7155492?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)