Senior MLOps Engineer

Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.

Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.

By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.com.

Senior MLOps Engineer

Later is the world’s most intelligent influencer marketing company. Built on a native, AI-powered platform and more than a decade of proprietary data, including billions of social interactions and $2.4B+ in verified influencer-driven purchases, Later helps brands choose the right creators, run fully managed campaigns, and know what will work before they launch.

Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good, they deliver results. Learn more at later.com.

About this position

We’re looking for a Senior MLOps Engineer to build and scale the infrastructure that takes Later’s machine learning and AI systems from prototype to production. You’ll own the pipelines that train, retrain, deploy, and serve the models behind our products, including forecasting, recommendation, and LLM-based and agentic features, across AWS and GCP.

You’ll join the Infrastructure team alongside our DevOps engineers and work closely with Data Science, Data Engineering, Platform, and Product Engineering. Data Science builds and iterates on models; you make sure they ship reliably, scale with demand, and retrain on the right cadence.

At first, expect roughly half your time on ML platform work and half on shared infrastructure. The split will shift toward ML as the practice grows into its own team. The role is mainly infrastructure and automation, with hands-on coding where it counts.

This is a strong fit for a senior engineer with a solid DevOps and cloud foundation and real experience putting ML systems into production, who wants to set the standard for MLOps at Later.

What you’ll be doing

Strategy & Architecture

• Own the MLOps roadmap with Infrastructure, Data Science, and Data Engineering leaders, aligned to product and AI growth plans.
• Evaluate and select tooling for orchestration, model registry, experiment tracking, LLM serving, and observability, and make clear build-or-buy recommendations.
• Shape the long-term architecture of our multi-cloud ML and data platform, with scalability, security, and cost built in from the start.

Building & Operating

• Design, build, and operate infrastructure that deploys and scales ML models in production, using CI/CD, GitOps, Kubernetes, and orchestration tools such as Airflow, Kubeflow, or Argo Workflows.
• Own automated training and retraining pipelines, including model registry, validation, deployment, and rollback, and agree on the retraining cadence with Data Science.
• Deploy and operate LLM inference (self-hosted on AWS Bedrock or managed APIs), including GPU capacity, cost controls, and performance monitoring.
• Manage training and inference infrastructure across AWS and GCP, including GPU workloads, using Terraform.
• Work with Data Engineering to source and prepare training data from BigQuery and S3, building pipelines and light transformations as needed.
• Build monitoring for model performance, latency, data drift, cost, and resource usage, using tools such as CloudWatch, Prometheus, and Grafana.