[Hiring] Senior MLOps Engineer @Later

Role Description

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.

Qualifications

• 7+ years of DevOps, cloud, or software engineering experience in production environments, including technical leadership.
• Deep expertise with Kubernetes (EKS), Helm, and microservices, including running GPU workloads on Kubernetes.
• Hands-on AWS experience and a track record of using Terraform for scalable, auditable infrastructure as code.
• Experience deploying, serving, and scaling ML models in production, including CI/CD for ML.
• Experience building data and training pipelines in partnership with Data Science and Data Engineering teams.
• Strong system design skills, including designing ML systems end to end and explaining the tradeoffs.
• Clear communication with technical and non-technical audiences.

Requirements

• 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.
• Shape the long-term architecture of our multi-cloud ML and data platform.
• Design, build, and operate infrastructure that deploys and scales ML models in production.
• Own automated training and retraining pipelines, including model registry, validation, deployment, and rollback.
• Deploy and operate LLM inference, including GPU capacity, cost controls, and performance monitoring.
• Manage training and inference infrastructure across AWS and GCP, including GPU workloads, using Terraform.
• Build monitoring for model performance, latency, data drift, cost, and resource usage.
• Support model versioning, experiment tracking, and reproducible training workflows.
• Lead troubleshooting of complex production and deployment issues.
• Set and document standards for infrastructure-as-code, CI/CD, observability, secure deployment, and ML workflow automation.
• Write runbooks and self-serve guides for Product and Data teams.
• Lead incident reviews and post-mortems that improve operations.
• Mentor engineers through pairing, code review, and feedback.
• Explain technical tradeoffs clearly to both technical and non-technical stakeholders.

Benefits

• Market-based & data-driven approach to compensation.
• Bi-annual evaluation of compensation including performance and market-related factors.
• Participation in various benefits plans as part of the overall compensation package.
• Salary range for candidates located in the US: $125,000-205,000 USD.
• Salary range for candidates located in Canada: $110,000-160,000 CAD.

Company Description

At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company. We embrace the importance of hiring and building teams for culture add rather than culture fit.

• We openly build and maintain unbiased hiring, pay, and promotion practices.
• We are committed to creating a diverse environment and are proud to be an equal opportunity employer.
• All applications will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, national origin, disability, or age.