Software Engineer, Full-Stack ML Infrastructure

Pramaana Labs•Palo Alto, CA

About The Position

Pramaana Labs is a frontier AI lab building a verification layer for AI. Founded in 2025 and headquartered in Palo Alto, the company formalizes complex human knowledge—including tax codes, legal rules, clinical guidelines, and security protocols—so AI answers can be traced, challenged, and proved. Its architecture combines foundation models with a symbolic world model encoded in Lean 4. Pramaana is seeking a full-stack engineer focused on infrastructure to build tools and systems that help 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 systems’ behavior, performance, and costs. The work spans interfaces, backend services, and infrastructure automation. This early-stage role has scope across the company. Work may expand to shared capabilities including 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, treating internal tools as products that are 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.
A product mindset for internal users, with attention to clear interfaces, sensible defaults, useful documentation, and reducing the steps needed to complete work.
Good engineering judgment and a collaborative approach, including writing maintainable code, testing important behavior, seeking review, communicating trade-offs, owning scoped problems, and recognizing when to seek help.
Strong software engineering fundamentals, practical problem-solving ability, and interest in DevOps and MLOps.

Nice To Haves

Experience with experiment tracking, evaluation pipelines, workflow orchestration, or model observability 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 the 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 to help teams diagnose issues, identify bottlenecks, and understand time and compute costs.
Help build and integrate shared platform services, including identity, access controls, LLM routing, API keys, budgets, and usage metering.
Evaluate and extend open-source or managed solutions where appropriate, and build custom software to address real gaps.
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 delivered systems.