Remote Software engineering Jobs · US

• Lead projects spanning the entire tech stack and mentor others.
• Collaborate through pull requests, demos, and working relationships.
• Drive projects from ideation through production with a positive attitude.

• Lead development and QA teams, ensuring timely delivery of high-quality software solutions.
• Oversee the design, implementation, and maintenance of SaaS and cloud-based products.
• Provide coaching, training, and performance feedback to team members.

Design, develop, and maintain robust APIs including public TTS API, internal APIs like Payment, Subscription, Auth and Consumption Tracking, ensuring they meet business and scalability requirements. Oversee the full backend API landscape, enhancing and optimizing for performance and maintainability. Collaborate on B2B solutions, focusing on customization and integration needs for enterprise clients.

• Lead a team of developer advocates and engineers focused on community trust.
• Guide the stewardship of open-source repos and developer tooling.
• Influence the Developer Platform and AI roadmap based on real-world needs.

• Ramp on full-stack architecture including backend services, frontend applications, APIs, and data flows.
• Take ownership of a feature area such as user dashboard, transaction flows, admin tools, or API integrations.
• Partner with product, design, and compliance teams to make pragmatic tradeoffs on user experience, performance, and regulatory requirements.

• Develop and maintain code for new and existing products, catering to both end-users and internal operational needs.
• Create and execute various tests to ensure software reliability and functionality.
• Monitor application performance and collaborate with the team to optimize software for better performance and scalability.

• Design, train, and operate ML systems that power the Tebra platform.
• Own the entire lifecycle from data exploration and model development to deployment.
• Push the boundaries of applied ML in healthcare, transforming messy real-world data into reliable automation.

• Own end-to-end implementation of AI-powered product features, from prototypes to production.
• Mentor other engineers on the team, leveling up the team as a whole.
• Collaborate across the organization to support shipping these features to production.

Design, develop, and maintain scalable services that support the diverse activities of a high-throughput laboratory. Develop both front-end and back-end applications Implement and manage CI/CD pipelines using GitLab. Collaborate closely with end-users and product managers to iterate on requirements and deliverables. Optimize architecture and infrastructure on the AWS cloud platform for performance, scalability, reliability, and cost-effectiveness.

• Lead a cross-functional team of ML engineers, data scientists, and MLOps professionals.
• Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment.
• Provide technical direction on large-scale model training, fine-tuning, and distributed systems design.