Craft Machine Inc

Build beautiful, well-tested features and services using technologies like React, Node.js, JavaScript/TypeScript, GraphQL, AWS, and more. We value learning and growing together instead of knowledge of every specific technology.
Work in small, empowered feature teams with teammates across Design, Product, and Data Engineers to drive complex features from start to finish.
Work closely with the designers to implement the best practices in UX/UI.
Have unlimited opportunities to take ownership of features and areas with increasing scope, as well as for technical and leadership growth.
Conduct code reviews, mentor and be mentored.
Advocate for modern engineering best practices, and use metrics to measure change.
Be encouraged to learn more about how a SaaS company works beyond engineering, to form stronger connections across disciplines, which ultimately leads to designing and building best-in-class products and services.

AWSGraphQLNode.jsPostgreSQLFull Stack DevelopmentHTMLCSSJavascriptReact.jsTypeScriptREST APIReactSaaS

Posted about 9 hours ago

Posted about 9 hours ago

🧭 Full-Time

🔍 Software Development

🔧 Requirements

3+ years of experience in a DevOps or MLOPs role (or similar experience), with a proven track record of implementing machine learning lifecycle solutions.
Experience using Terraform or other infrastructure-as-code tools.
Experience with designing and optimizing infrastructure for machine learning workloads, including GPU cost optimization.
Familiar with tools and practices for model management, deployment, and monitoring, such as MLflow, ElasticSearch, or OpenSearch.
Able to think holistically about data platforms and their integration with machine learning workflows.
Deep knowledge of Amazon Web Services (AWS) Infrastructure along with experience with at least a part of the other components of our technology stack: Airflow, Apollo, CircleCI, Cloudflare, Databricks, Docker, GraphQL, Pandas, PyTorch, Python, spaCy, Transformers.

💡 Responsibilities

Contribute to and implement the DevSecOps and MLOps strategies, working with multiple stakeholders that are focused on automating and securing the development lifecycle.
Design and manage the entire MLOps lifecycle, including data versioning, model training, deployment, and monitoring.
Implement tools and frameworks for model versioning, tagging, and QA processes.
Optimize infrastructure for model training and inference, focusing on cost efficiency and avoiding vendor lock-in .
Automate security and compliance measures across the machine learning lifecycle, ensuring seamless integration with CI/CD pipelines.'
Develop monitoring systems for production models, including active learning workflows to flag problematic records for further verification.
Build and manage scalable infrastructure to support machine learning workloads, including data storage, versioning, and evaluation platforms.
Perform threat modeling, risk assessment, and code reviews to assess cybersecurity implications.

Posted about 9 hours ago