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Great Sky is a technology startup rebuilding AI from first principles, pursuing neuroscience-inspired hardware and algorithms to overcome scaling challenges in AI systems.
You will be an AI Frameworks Engineer responsible for building the core software layer connecting research ideas to working, testable, scalable AI systems. This is a software engineering role focused on the internal AI framework: abstractions, APIs, graph execution paths, training strategies, validation systems, and test infrastructure that make the research stack usable and reliable.
Key responsibilities include:
- Design, implement, and maintain core AI framework abstractions for defining, training, validating, and executing novel graph-structured models.
- Build clean Python APIs for training workflows, including loss objects, model runtime bundles, optimizer/training strategy interfaces.
- Develop and maintain JAX and PyTorch infrastructure for model execution, customizable training protocols, numerical parity tests, and reusable forward-pass utilities.
- Design validation systems that catch incompatible model/loss/solver/topology combinations early with clear diagnostics.
- Organize research code into stable, well-tested framework components without slowing exploration.
- Create clean seams between research-mode and production harness workflows to support ad hoc experiments, custom training loops, and cloud training runs.
- Write high-quality tests for numerical behavior, protocol conformance, registry behavior, graph execution and training loops.
- Collaborate closely with researchers and hardware/software engineers to translate evolving requirements into robust software architecture.
- Improve developer ergonomics through documentation, examples, error messages, package organization, type boundaries, and API consistency.
- Help shape the broader AI software architecture connecting model design, simulation, training, validation, and hardware execution.
The role offers a path toward broad technical ownership within Great Sky's AI software stack. As the team grows, you may help define framework architecture, set engineering standards, mentor other engineers, and lead major infrastructure projects. For the right person, this is a chance to build foundational infrastructure for a new kind of AI platform.
QUALIFICATIONS
Required:
- Bachelor's or Master's degree in Computer Science or related field, or equivalent professional experience.
- Strong Python software engineering skills with experience building libraries, frameworks, or infrastructure used by other engineers or researchers.
- Hands-on experience with at least one modern ML framework, especially JAX or PyTorch.
- Proficiency with training-loop internals: forward passes, losses, gradients, optimizers, metrics, parameter/state handling, and numerical testing.
- Experience designing clean APIs and abstractions in complex codebases.
- Ability to reason about graph-structured computation, model execution, dependency boundaries, and runtime state.
- Strong testing instincts: unit tests, integration tests, regression tests, numerical parity tests, and clear failure modes.
- Ability to work in a research-heavy environment where requirements evolve and the right abstraction emerges through iteration.
- Excellent written and verbal communication skills, especially translating between research ideas and maintainable engineering designs.
Preferred:
- Prior experience in an AI technology environment.
- Experience with typed Python, protocol-oriented design, dataclasses, Pydantic, package refactors, or public/internal SDK design.
- Familiarity with multi-node and multi-GPU training for JAX or PyTorch.
- Experience with custom kernel writing in Triton/Cuda/Pallas.
- Experience designing plugin or registry systems for losses, optimizers, training strategies, model components, or execution backends.
- Experience with compiler-adjacent or graph-adjacent systems: computational graphs, schedulers, IRs, topology validation, graph transformations, or hardware-aware execution.
- Experience working near hardware teams, accelerator teams, simulation teams, or low-level performance work.
- Familiarity with containers, cloud execution, job orchestration, experiment tracking, or production training harnesses.
