DevOps Engineer (MLOps)

DevOps Engineer (MLOps)

Remote / Columbus, OH / Chicago, IL / Austin, TX / Los Angeles, CA / Boston, MA

Engineering /

Full-time /

Remote

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About the Engineering Organization: The Engineering Team at Loop is a balance of agility, consistency, and performance. These are the pillars that allow the team to constantly and consistently deliver value that matters to customers. That customer intimacy is what allows our engineering teams to be the best in our space, and bring the best ideas to the market.

About the Role: We are seeking a DevOps (MLOps) Engineer to pioneer and mature our machine learning operations capabilities, focusing on building robust infrastructure and deployment pipelines in AWS. This critical role will own the infrastructure underlying all of our productionalized ML models , from deployment to monitoring, fostering seamless collaboration between our machine learning and broader engineering teams. The MLOps engineer will work closely with ML engineers to keep state-of-the-art, mission-critical ML models up-to-date, scalable, and observable.

Our Blended Work Environment: At Loop, we’re intentional about the way we work so that we can do our best work. We call this our Blended Working Environment. We work from our HQ in Columbus, OH, or one of our Hub or Secluded locations, and are distributed throughout the United States, select Canadian provinces, and the United Kingdom. For this position, we’re looking for someone to join us in Columbus, OH; Chicago, IL; Austin, TX; Los Angeles, CA; or fully remote.

Our Tech Stack: AWS Cloud (Kubernetes, Serverless architecture, Redis, Aurora, DynamoDB, and identity management), Docker, MLFlow, Gitlab, Airflow, PHP/Laravel, Linux, Terraform, Datadog, Snowflake, dbt

What You'll Do:

• Design and implement scalable CI/CD pipelines for machine learning models within our AWS infrastructure, driving automated builds, deployments, and engineering excellence.
• Establish and evolve ML operational best practices in a greenfield environment, defining the standards for model versioning, reproducibility, and MLOps maturity.
• Collaborate closely with Machine Learning Engineers to understand model requirements and provide expert guidance on infrastructure, deployment strategies, and operationalizing their models.
• Implement and manage comprehensive monitoring and observability solutions for deployed ML models using tools like Datadog, ensuring high performance, accuracy, and quick issue resolution.
• Maintain and optimize ML model repositories to ensure efficient versioning and management of all model artifacts throughout their lifecycle.
• Drive the adoption of Infrastructure as Code (IaC) principles for ML infrastructure, ensuring reusability, consistency, and reliability across environments.
• Participate in the broader DevOps team ceremonies and planning, integrating ML Ops initiatives seamlessly into the overall engineering roadmap.

Your Experience:

• 5+ years of experience in DevOps or MLOps Engineering roles, with at least 2+ years dedicated to hands-on experience specifically with machine learning operations, including enterprise-grade deep learning architectures (e.g., transformers, graph NNs, VAEs).
• Bachelor’s degree or higher in Computer Science, Mathematics, Statistics, or a related quantitative discipline, or equivalent practical experience, is highly preferred.
• Deep expertise in AWS infrastructure and services, with a proven track record of deploying and managing scalable ML workloads in the cloud.
• Strong proficiency in Python and extensive experience with key machine learning libraries such as PyTorch, Pandas, and scikit-learn.
• Extensive experience with containerization technologies (ex: Docker) for packaging and deploying ML models.
• Demonstrated experience with ML lifecycle management platforms such as MLflow.
• Proven ability to thrive as a self-starter in ambiguous, greenfield environments, taking initiative and delivering solutions with minimal oversight.
• Excellent collaboration and communication skills, with a demonstrated ability to act as a critical bridge between machine learning, data science, and core engineering teams.

$123,200 - $184,800 a year

We know that making decisions about your career and compensation is a huge deal. Because of that, we’re incredibly thoughtful about our compensation strategy. We want you to feel safe and excited, but also comfortable with the compensation package of a startup. We’ve outlined some important information for you here, but please know there’s a lot more to compensation than we can cover in this job posting.

The posted salary range is the base salary for this opportunity. The salary range is subject to change, and may be adjusted in the future.

The actual annual salary paid for this position will be based on several factors, including, but not limited to: your prior experience and skills related to the position, geographic location, company needs, current market demands, and your total compensation goals.

Great humans deserve great benefits. At Loop, you’ll be eligible for benefits such as: medical, dental, and vision insurance, flexible PTO, company holidays, sick & safe leave, parental leave, 401k, monthly wellness benefit, home workstation benefit, phone/internet benefit, and equity.

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Loop Story

In a perfect world, Loop wouldn't exist. If we had our way, we'd live in a world where we're mindful about how we consume, we love every product we own, and we share values with the brands who create them. In reality, commerce isn't perfect and often breaks. Loop creates second chances.

We're starting by revolutionizing the post-purchase experience. We've taken one of the most fragile commerce interactions - returns - and turned it into something consumers actually love, and that deepens our connection to brands and products.

We take connection seriously on the inside, too. We're building a work experience that allows you to Be A Human First and prioritizes empathy and wellbeing. We view Loop as a special place in your career to shape the future of an industry and become a better person while doing it. You can grow faster here in a shorter amount of time - we'll give you space and trust you to fill it.

Learn more about us here: https://loopreturns.com/careers.

You can review our privacy notice here.

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