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.
About this position:
We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.
This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.
What you'll be doing:Strategy
- Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
- Establish best practices for model lifecycle management, deployment standards, monitoring, and governance.
- Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
- Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.
Technical/ Execution
- Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
- Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
- Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
- Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
- Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
- Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.
Team / Collaboration
- Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
- Translate data science experimentation needs into production-ready infrastructure solutions.
- Serve as the technical bridge between ML experimentation and productized deployment.
- Share knowledge and best practices to elevate ML maturity across teams.
Research/Best Practices
- Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
- Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
- Contribute to governance, reproducibility, and auditability standards for ML systems.
- Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.
What success looks like:
- ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
- CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
- Model performance, drift, and infrastructure health are proactively monitored and observable.
- Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
- Data scientists report improved velocity, reproducibility, and confidence in deploying models.
- ML systems are secure, compliant, and aligned with evolving product and AI strategy.
What you bring:
- 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
- Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
- Prov