Senior Software Engineer - Platform & MLOps

In this role, you will champion the developer experience by building the internal platform and tooling that every AI engineer at our company trusts and relies on. You'll craft intuitive dashboards, user-friendly CLI tools, clean APIs, and elegant automation.

About the role

• Craft Beautiful Internal UIs: Develop intuitive and accessible internal platform interfaces—such as dashboards, model registries, and experiment trackers—using React and Next.js to elevate our internal developer experience.
• Nurture Backend Infrastructure: Design and support reliable backend services and APIs in Python, ensuring our application teams can seamlessly tap into the platform's full capabilities.
• Create and refine thoughtful automation for model deployment, rollbacks, and scaling workflows using scripts, APIs, and Infrastructure as Code (IaC), embedding safety and predictability into every release.
• Weave monitoring and observability tools (such as Grafana, Prometheus, DataDog, and LangSmith) directly into the platform, giving our teams the clarity and insights they need to succeed.
• Write comprehensive testing, evaluation, and validation logic for machine learning pipeline components to ensure high reliability and product excellence.
• Partner with Internal Teams: Act as a supportive resource for our internal engineering community, listening to their experiences and gathering empathetic feedback to continuously iterate on our tooling.
• Share Knowledge Generously: Foster a collaborative environment by documenting systems clearly and contributing to engineering wikis and runbooks so that the entire team can grow together.

Responsibilities

• Ensure that deploying and operating AI models is a seamless, safe, and empowering self-service journey, allowing our entire engineering community to thrive and scale together.
• Craft Beautiful Internal UIs: Develop intuitive and accessible internal platform interfaces—such as dashboards, model registries, and experiment trackers—using React and Next.js to elevate our internal developer experience.
• Nurture Backend Infrastructure: Design and support reliable backend services and APIs in Python, ensuring our application teams can seamlessly tap into the platform's full capabilities.
• Create and refine thoughtful automation for model deployment, rollbacks, and scaling workflows using scripts, APIs, and Infrastructure as Code (IaC), embedding safety and predictability into every release.
• Weave monitoring and observability tools (such as Grafana, Prometheus, DataDog, and LangSmith) directly into the platform, giving our teams the clarity and insights they need to succeed.
• Write comprehensive testing, evaluation, and validation logic for machine learning pipeline components to ensure high reliability and product excellence.
• Partner with Internal Teams: Act as a supportive resource for our internal engineering community, listening to their experiences and gathering empathetic feedback to continuously iterate on our tooling.
• Share Knowledge Generously: Foster a collaborative environment by documenting systems clearly and contributing to engineering wikis and runbooks so that the entire team can grow together.

Requirements

• A deep comfort with Python, paired with hands-on experience building dependable backend services and clean REST APIs.
• A genuine appreciation for crafting responsive dashboards and internal tools using TypeScript and React.
• Experience caring for and managing machine learning-based applications within a live production ecosystem.
• A foundational comfort navigating cloud platforms (AWS, GCP, or Azure), alongside a basic understanding of Docker and Kubernetes.
• A history of working hand-in-hand with data or machine learning engineering teams, respecting and learning from different technical perspectives.
• A solid foundational understanding of databases, job queuing systems, and asynchronous workflows.
• A natural curiosity and comfort when facing open-ended or ambiguous technical challenges, with the ability to turn abstract ideas into structured, clear steps forward.

Qualifications

• 5+ years of experience in full-stack engineering.
• A passion for building world-class products and a commitment to challenging the status quo.
• A collaborative spirit and a history of working hand-in-hand with data or machine learning engineering teams.
• A strong foundation in Python and a deep understanding of backend services and clean REST APIs.
• A genuine appreciation for crafting responsive dashboards and internal tools using TypeScript and React.
• An understanding of system flow and a solid foundational understanding of databases, job queuing systems, and asynchronous workflows.
• A natural curiosity and comfort when facing open-ended or ambiguous technical challenges, with the ability to turn abstract ideas into structured, clear steps forward.

Skills

• Python
• React
• TypeScript
• Next.js
• Python backend services
• REST APIs
• Cloud platforms (AWS, GCP, or Azure)
• Docker
• Kubernetes
• Monitoring and observability tools (Grafana, Prometheus, DataDog, LangSmith)
• Machine learning pipeline components
• Testing, evaluation, and validation logic
• Collaboration and communication skills
• Problem-solving skills

Benefits

• A competitive base pay
• Medical Benefits
• Discretionary incentive plan based on individual and company performance
• Focus on development: Access to a learning & development platform and opportunity for you to own your career pathways
• Flexible work policy

Pay

The pay range is between $152,000 - $230,000 USD as a base salary offering annually.

Schedule

Not specified.