Give AI coding and software engineering agents access to structured engineering tasks, repository-level workflows, executable environments, and expert-reviewed outcomes.
A model can generate syntactically correct code and still struggle with a large repository, an unfamiliar dependency, a failing test, or a change that introduces a regression. Einstein Hire captures the engineering around the code; not just the final answer.
Engineers work across repositories, dependencies, documentation, tests, and existing implementation patterns before making a change.
Executable environments expose compiler errors, test failures, runtime behavior, and other signals that static code evaluation can miss.
Training and evaluation workflows can capture failed attempts, debugging steps, alternative approaches, and expert corrections.
Move beyond isolated coding prompts with data designed around the tasks software engineers actually perform.
Evaluate how models understand unfamiliar codebases, trace dependencies, identify relevant files, and plan changes across multiple components.
Capture structured interactions across prompts, code changes, terminal commands, test results, compiler output, and debugging iterations.
Support workloads involving APIs, databases, cloud infrastructure, CI/CD, containers, distributed systems, and other engineering environments.
Extend training and evaluation into topics such as architecture diagrams, UI states, logs, screenshots, and technical documentation where relevant.
We partner with your ML researchers to design deterministic grading rubrics.
Let models work with the same types of tools they encounter in software development.
Successful code changes are only part of the engineering process.
Evaluate changes through executable test environments rather than relying only on text-based judgments.
The most valuable training data isn't manufactured for a benchmark. It comes from real work: the decisions professionals make, the context that shapes them, and the trade-offs behind each choice.
We define exact task architectures, reasoning requirements, and evaluation rubrics tailored to your model.
Create task specifications, evaluation criteria, environments, and reviewer guidelines around those requirements.
Run engineering workflows in controlled environments and capture relevant interactions, outputs, and outcomes.
Use qualified reviewers to assess solutions, identify failure modes, and validate task-specific signals.
Package validated data and evaluation assets in formats compatible with your existing training and evaluation stack.
Give your models the repositories, tools, execution environments, and expert feedback they need to handle real software engineering workflows; from planning and implementation to debugging and verification.
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