Remote Senior ML Platform Engineer

Doist; West Hollywood (CA); Hybrid; USD 190,000 - 246,000; Full time

Job summary

Doist seeks a senior ML infrastructure engineer to design and scale the platform that supports experimentation, training, deployment, and monitoring of ML models across large datasets. You will build data pipelines, APIs, and back-end services, collaborating with ML and software engineers to deliver reliable, self-service infrastructure.

You will optimize compute and storage, ensure observability, and lead CI/CD and GitOps workflows, including Ray Serve and Triton deployments.

Qualifications

• Bachelor’s degree or its U.S. equivalent in Computer Science, Computer Engineering, or a related field, plus 5 years of professional experience as a Machine Learning Engineer, Site Reliability Engineer, or ML infra/backend software engineer.

Responsibilities

• Design and build scalable ML platform systems and data infrastructure using distributed data technologies (Spark, Kafka, Flink, Databricks).
• Develop and maintain APIs (REST, gRPC, GraphQL) for internal ML platform services.
• Implement model evaluation, validation, and quality assurance processes including A/B testing frameworks.
• Mentor junior engineers and participate in technical interviews.
• Collaborate with cross-functional teams to support ML-enabled product features.

Skills

Distributed ML platforms

Python

Scala

Java

Go

Spark

Kafka

Flink

Databricks

AWS

Azure

GCP

Terraform

Terragrunt

Helm

Docker

Prometheus

Grafana

Grafana Mimir

Ray Serve

Triton

Delta Lake

Redis

DynamoDB

Data warehouses

Leadership

GitOps

Education

Bachelor’s degree in Computer Science or related field

Master’s degree in Computer Science or related field

Tools

Jenkins

Buildkite

Job description

Doist seeks a senior ML infrastructure engineer to design and scale the platform that supports experimentation, training, deployment, and monitoring of ML models across large datasets. You will build data pipelines, APIs, and back-end services, collaborating with ML and software engineers to deliver reliable, self-service infrastructure.

You will optimize compute and storage, ensure observability, and lead CI/CD and GitOps workflows, including Ray Serve and Triton deployments.