Software Engineer

Your impact

Build the software that turns frontier models, scientific data, and compute into a dependable molecular discovery platform.

As a Software Engineer, you will help scientists run ambitious design campaigns, understand their results, and move promising molecules toward experimental testing. Researchers across Pythia will depend on the systems you build to move from an idea to trustworthy evidence more quickly.

The role is intentionally broad because Pythia is a small startup. You will work across developer tools, model integrations, data systems, distributed execution, and the infrastructure underneath them, taking ownership from understanding a problem through implementation, deployment, and operation.

What you will do

• Build the platform for scientific work. Develop services, APIs, command-line tools, and interfaces that help researchers configure, launch, monitor, and debug computational workflows.
• Make distributed execution dependable. Improve scheduling, task state management, retries, caching, and recovery across cloud and on-premises compute.
• Bring research capabilities into production. Integrate models and computational biology tools through clear interfaces, managed dependencies, versioned environments, and scientific validation.
• Build reliable data foundations. Work with researchers on scientific data ingestion, processing, storage, access, and provenance so results remain traceable and reproducible.
• Work across system boundaries. Diagnose problems across Python and Rust, containers, storage, networking, and CPU/GPU execution, and improve the performance and cost of real workloads.
• Own and improve the systems the team depends on. Make practical design decisions, write meaningful tests, build observability, resolve production issues, and turn recurring friction into reusable tools and simpler systems.

What you bring

You are a pragmatic engineer who enjoys following a problem across layers and disciplines until the whole workflow works. You balance reliability, performance, simplicity, and delivery speed, and you communicate well with researchers whose needs evolve as they learn.

• A track record of building and maintaining production software, with ownership beyond the initial implementation.
• Strong programming fundamentals and fluency in Python or a systems language such as Rust, C++, or Go, with the ability to become effective across our stack.
• Sound reasoning about concurrency, state, data integrity, resource use, and failure modes.
• Experience with Linux, containers, and cloud or distributed systems.
• Clear communication, curiosity, initiative, and a habit of checking assumptions against evidence.

Depth in one or more of these areas matters more than experience with every technology. A background in biology is useful, but curiosity and a willingness to learn the scientific context are essential.

Experience that would help

• Workflow engines, distributed schedulers, storage systems, or developer platforms.
• ML model deployment, GPU workloads, scientific computing, or high-performance computing infrastructure.
• Large-scale data processing, reproducible computation, or integrating research software into production, including effective workflows with coding agents and automated checks that make their work easier to inspect and validate.

Our working environment

Our stack includes Python, Rust, and TypeScript, with Docker and AWS alongside on-premises GPU infrastructure. Work ranges from scientific libraries and model execution to backend services and researcher-facing tools.

AI coding tools are part of our engineering workflow. We use them to explore, implement, test, and automate work, while remaining accountable for system design, correctness, maintainability, and the scientific outcomes our software supports.

Why Pythia

Your work can unlock new experiments, shorten the path from an idea to a result, and make new scientific capabilities available across the company. In a small team, improvements to foundational software have an immediate and visible effect.

We are a small, interdisciplinary team. We value people who take ownership, move between disciplines, check assumptions against evidence, and bring others into decisions. You will have room to shape both the work and how the team works.