Jobs in the United States

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UW
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Added 1 min ago
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Later

Senior MLOps Engineer

Boston, MA, United States

Apply on job-boards.greenhouse.io About employer

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Job details

Experience: 7+ years of DevOps, cloud, or software engineering experience in production environments, including technical leadership: leading projects, setting direction, and mentoring others.
Posting language: English
Posted: May 19, 2026

Original job description

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.
Support model versioning, experiment tracking (e.g., MLflow, SageMaker), and reproducible training workflows.
Lead troubleshooting of complex production and deployment issues, and take part in the Infrastructure on-call rotation.

Leadership & Collaboration

Set and document standards for infrastructure-as-code, CI/CD, observability, secure deployment, and ML workflow automation.
Write runbooks and self-serve guides so Product and Data teams can deploy, monitor, and troubleshoot with more autonomy.
Lead incident reviews and post-mortems that improve how we operate.
Mentor engineers through pairing, code review, and feedback.
Explain technical tradeoffs clearly to both technical and non-technical stakeholders.

What success looks like

First 30 days: Learn and support. You learn Later’s cloud infrastructure, Kubernetes environments, CI/CD workflows, and the data and ML platform, including how models are trained and deployed today. You start supporting existing pipelines and observability tools. By day 30 you know where the platform’s biggest gaps are and how Product and Data teams use it.

First 60 days: Contribute and improve. You own infrastructure and automation work across Kubernetes, AWS, CI/CD, and the ML platform. You make deployment pipelines more consistent and automated, improve GPU provisioning and monitoring for training and inference, and close gaps in alerting and runbooks. You’re already mentoring teammates on the patterns you’re introducing.

First 90 days: Own and scale. You own the path from a finished model to production. By day 90:

At least one production model is deployed, retrained, and rolled back through an automated pipeline, with no manual steps.
Drift, latency, and cost alerts are live for production models.
A first MLOps roadmap has been reviewed with Infrastructure, Data Science, and Data Engineering leads.
Security and compliance checks are built into the ML deployment path.

What you bring

7+ years of DevOps, cloud, or software engineering experience in production environments, including technical leadership: leading projects, setting direction, and mentoring others. People management is not required.
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.

Nice to have

GCP experience, especially BigQuery.
Amazon SageMaker.
Self-hosted LLM deployments on Kubernetes (e.g., vLLM, KServe).
Orchestration and experiment tracking tools such as Airflow, Kubeflow, Argo Workflows, or MLflow.

If you don’t meet every requirement but this role sounds like you, we’d still love to hear from you.

How you work

Driven by Impact: You deliver results that matter - prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast - challenging the status quo with a mindset of improvement.
Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive - removing roadblocks, sharing insights, and keeping morale high.
Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only. The salary range listed for this role reflects the zones that applies to the location of this posting.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted.

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary range:

For candidates located in the US: $125,000-205,000 USD For candidates located in the Canada: $110,000-160,000 CAD

Where we work:

We have offices in Boston, MA; Vancouver, BC; and Vancouver, WA. For select positions, we are open to hiring fully remote candidates. We post our positions in the location(s) where we are open to having the successful candidate be located.

Diversity, inclusion, and accessibility:

At Later, we are committed to fostering a culture rooted in an inclusion-first mindset at every level of the company, embracing 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 to create a foundation for an equitable workplace, paving the way for systemic change.

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. Please let us know if you require any accommodations or support during the recruitment process.

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Jobs in the United States