Remote AWS Machine Learning Engineer

Shape the Future of Intelligent Cloud Solutions

What if your next model didn’t just predict trends—it transformed how companies operate? As a Remote AWS Machine Learning Engineer, you’ll architect, deploy, and optimize ML pipelines that unlock actionable insights from massive data lakes. Here, your curiosity fuels innovation, your experiments lead to breakthroughs, and your expertise impacts how thousands of users experience technology every day. This is the space where ambition meets execution, and you’ll never be left wondering if your work matters. With an annual salary of $126,869, your skills and vision are valued from the start.

Role Overview

You’re the person who sees hidden potential in raw data. When you join, you’ll design, train, and scale machine learning models using AWS SageMaker, Lambda, and Glue. Every model you ship will drive more intelligent automation, tighter personalization, or faster business decision-making for clients worldwide. Your code isn’t just robust—it’s crafted for seamless production, with CI/CD pipelines, code reviews, and precise documentation as part of your workflow.

What Sets This Role Apart

• You’ll partner with data scientists, software engineers, and product strategists—no silos here. Every initiative begins with collaboration and culminates in a measurable impact.
• Your work will streamline data ingestion, accelerate analytics, and help launch products that solve real-world problems. Imagine launching a model that reduces client churn by 15% or a recommendation engine that powers personalized experiences for millions.
• Expect variety: you’ll experiment with natural language processing, computer vision, anomaly detection, and time-series forecasting, depending on what’s most needed. There’s no “one size fits all” here—just the freedom to use the right tool for every job.

Key Responsibilities

Build and Deploy Machine Learning Models

• Architect scalable pipelines for training, validation, and deployment on AWS
• Automate model retraining and monitoring using Lambda, Step Functions, and CloudWatch
• Fine-tune hyperparameters for maximum performance and cost efficiency

Collaborate for Maximum Impact

• Work closely with engineering, product, and UX teams to translate business challenges into technical solutions.
• Share knowledge through pair programming and brown-bag sessions—your ideas will reach far beyond your desk.
• Communicate your findings in a way that enables stakeholders from diverse backgrounds to act on your insights effectively.

Drive Continuous Innovation

• Explore the latest AWS ML services and open-source libraries to keep our stack modern and competitive.
• Advocate for MLOps best practices, including reproducibility, version control, and CI/CD.
• Identify opportunities for automation across data processing and model deployment workflows.

The Tools You’ll Use

You’ll be hands-on with the AWS ML stack: SageMaker, Glue, Redshift, Lambda, S3, and more. You’ll code primarily in Python, but you’re comfortable integrating with Java or Scala when a data engineering challenge calls for it. You’ll document your decisions in Confluence, track your work in Jira, and celebrate milestones with a team that believes in thoughtful feedback and real recognition.

The Work Environment

We’re remote-first by design. That means flexible hours, async-first communication, and a workspace that fits your life—not the other way around. You’ll join a global team that values transparency and accountability. Here, you simplify complex ideas—whether it’s over Zoom or Slack—so everyone moves forward together. Our engineering culture means you’ll have the focus time to tackle deep work but the camaraderie to keep it fun.

What You Bring

• Deep hands-on experience building and productionizing ML models on AWS
• Mastery in Python (with strong knowledge of relevant libraries like TensorFlow, PyTorch, or scikit-learn)
• You turn ambiguous data into clear strategies, always thinking about end-user outcomes
• Familiarity with MLOps, automation, and agile iteration—our workflow moves quickly, but you’ll always have space to focus deeply
• You make complex ideas actionable, translating technical findings for business and non-technical partners
• Bachelor’s or Master’s in Computer Science, Engineering, Math, or related field (or equivalent experience)

Impact, Growth, and Recognition

Here, your growth isn’t limited by job title or geography. You’ll have a say in the roadmap, join project retrospectives, and see how your code shapes product features that users love. Every contribution—whether it’s a new model, a technical talk, or a documentation breakthrough—earns real recognition. You’ll have access to the latest AWS resources, learning stipends, and a culture that celebrates both wins and learning moments.

Ready to Build What’s Next?

If you want your work to shape products that power the future, this is your invitation. Let’s turn ideas into impact—from wherever you do your best work.