Company
Pramaana Labs is a frontier AI lab building the verification layer for AI. Founded in 2025 and headquartered in Palo Alto, we turn complex human knowledge, including tax codes, legal rules, clinical guidelines and security protocols, into a formal representation, so every AI answer can be traced, challenged, and proved. Pramaana's architecture pairs foundation models trained to formalize and reason with a symbolic world model encoded in Lean 4. Pramaana was founded by a team out of Google, DeepMind, and Glean, combining frontier AI researchers and formal methods experts.
The Role
We’re looking for a full-stack engineer with focus on infrastructure to build the tools and systems that help Pramaana’s engineers and researchers develop, test, deploy, and operate their work more effectively.
Your initial focus will be DevOps and MLOps: improving development environments and deployment workflows, making experiments and evaluations reproducible, and helping teams understand the behavior, performance, and cost of AI systems. You’ll build across interfaces, backend services, and infrastructure automation—not just maintain scripts or configure dashboards.
This is an early-stage role with scope across the company. As needs evolve, your work will extend into shared capabilities such as identity and access, LLM gateways, API-key management, training and inference platform maintenance, usage metering, and internal applications. You’ll work directly with the people using these systems, turning recurring friction into reliable, self-serve tools.
We’re looking for someone who treats internal tools as products: useful, understandable, well-maintained, and built around the workflows of the people who depend on them.
What You'll Do
Improve how software gets built and deployed. Develop repeatable local environments, automated tests, CI/CD pipelines, and deployment workflows. Help make configuration, secrets, releases, and rollbacks easier to manage safely.
Build tools for the AI development lifecycle. Create workflows for running experiments and evaluations, versioning datasets and configurations, tracking results and artifacts, and deploying or integrating model services. Make runs easier to reproduce, compare, and investigate. Help researchers get from a recipe to a running job, and from a failed job to the cause..
Create full-stack, self-serve applications. Build interfaces and APIs that let teams launch jobs, manage configurations, inspect results, and troubleshoot failures without relying on repeated manual assistance.
Make systems observable and costs understandable. Connect logs, metrics, traces, and usage data so teams can diagnose issues, identify bottlenecks, and understand where time and compute are being spent.
Contribute to shared platform services. Help build and integrate identity, access controls, LLM routing, API keys, budgets, and usage metering across Pramaana’s products and internal workflows.
Use existing tools thoughtfully. Evaluate and extend open-source or managed solutions where they fit. Build custom software where it solves a real gap, rather than recreating infrastructure unnecessarily.
Improve tools through direct feedback. Work alongside engineers and researchers to identify recurring problems, prioritize improvements, and measure whether tools reduce setup time, manual work, and failed runs. Maintain tests, documentation, and operational guidance for what you ship.
You Will Thrive in This Role If You Have
Minimum 2-3 years of software engineering experience, including hands-on work with backend services, full-stack applications, internal tools, or deployment automation.
A solid foundation in building software across the stack. You can develop 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. You’ve deployed applications using containers and a cloud platform such as AWS, worked with CI/CD, and used logs or metrics to investigate problems.
Some hands-on exposure to AI or ML workflows. This could include integrating model APIs, running evaluations, tracking experiments, or operating model-backed applications through work or substantial projects. You don’t need to be an ML researcher.
A product mindset for internal users. You care about clear interfaces, sensible defaults, useful documentation, and reducing the number of steps it takes someone to get their work done.
Good engineering judgment and a collaborative approach. You write maintainable code, test important behavior, ask for review, and communicate trade-offs. You can take ownership of a scoped problem while recognizing when to seek help.
Especially Strong Candidates May Also Have
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: 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 that other engineers or researchers use regularly.
You don’t need experience with every system listed above. Strong software engineering fundamentals, practical problem-solving, and an interest in DevOps and MLOps matter more than matching every tool.