Software Engineer, Full-Stack ML Infrastructure

Pramaana Labs • Palo Alto, CA

3d

About The Position

Pramaana Labs is hiring a full-stack engineer focused on infrastructure to build tools and systems that help its engineers and researchers develop, test, deploy, and operate their work. The initial focus is DevOps and MLOps: improving development environments and deployment workflows, making experiments and evaluations reproducible, and helping teams understand AI system behavior, performance, and cost. The role spans interfaces, backend services, and infrastructure automation.

As an early-stage role with company-wide scope, responsibilities may expand to shared capabilities such as identity and access, LLM gateways, API-key management, training and inference platform maintenance, usage metering, and internal applications. The engineer will work directly with system users to turn recurring friction into reliable, self-serve tools. Pramaana seeks someone who treats internal tools as products: useful, understandable, well-maintained, and designed around users’ workflows.

Requirements

• At least 2–3 years of software engineering experience, including hands-on work with backend services, full-stack applications, internal tools, or deployment automation.
• Ability to build software across the stack, including services and automation in Python or TypeScript, work with APIs and databases, and build usable interfaces with React or a similar framework.
• Practical cloud and deployment experience, including deploying applications with containers and a cloud platform such as AWS, working with CI/CD, and using logs or metrics to investigate problems.
• Some hands-on exposure to AI or ML workflows, such as integrating model APIs, running evaluations, tracking experiments, or operating model-backed applications through work or substantial projects.
• A product mindset for internal users, including care for clear interfaces, sensible defaults, useful documentation, and reducing the steps users need to complete work.
• Good engineering judgment and a collaborative approach, including writing maintainable code, testing important behavior, seeking review, communicating trade-offs, and taking ownership of scoped problems while recognizing when to seek help.

Nice To Haves

• Experience with experiment tracking, evaluation pipelines, workflow orchestration, or model observability using tools such as MLflow, Langfuse, or Temporal.
• Experience with infrastructure as code, container orchestration, or telemetry tooling, such as Terraform, Kubernetes, or OpenTelemetry.
• Experience running GPU workloads on Kubernetes, including job queueing and preemption, multi-node training with NCCL over EFA or InfiniBand, GPU node health and driver management, or serving models with vLLM or SGLang.
• Experience implementing authentication, role-based access, API-key management, image provenance, LLM gateways, usage and cost tracking, or hardening public endpoints.
• Experience adapting open-source software or building developer tools used regularly by engineers or researchers.

Responsibilities

• Develop repeatable local environments, automated tests, CI/CD pipelines, and deployment workflows; improve safe management of configuration, secrets, releases, and rollbacks.
• Create workflows for running experiments and evaluations, versioning datasets and configurations, tracking results and artifacts, and deploying or integrating model services.
• Make experiment and evaluation runs easier to reproduce, compare, and investigate; help researchers launch jobs and diagnose failures.
• Build full-stack, self-serve interfaces and APIs for launching jobs, managing configurations, inspecting results, and troubleshooting failures.
• Connect logs, metrics, traces, and usage data so teams can diagnose issues, identify bottlenecks, and understand time and compute costs.
• Help build and integrate identity, access controls, LLM routing, API keys, budgets, and usage metering across products and internal workflows.
• Evaluate and extend open-source or managed solutions where appropriate, and build custom software where it addresses a genuine gap.
• Work with engineers and researchers to identify recurring problems, prioritize improvements, and assess whether tools reduce setup time, manual work, and failed runs.
• Maintain tests, documentation, and operational guidance for shipped tools.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed