Gorilla Logic is looking for a Senior Software Engineer, Full-Stack with strong backend expertise in Python to join an established engineering team building and scaling an automated evaluation platform for agent-oriented software products.

This is a backend-heavy, hands-on engineering role focused on building production services and durable workflows that evaluate third-party applications through APIs and browser-driven interactions. You will work with significant autonomy, taking technical problems from discovery and design through implementation, testing, and production release.

You’ll collaborate directly with a US-based client team and cross-functional partners while working with technologies including Python, FastAPI, durable execution frameworks, Docker, Kubernetes, Argo CD, and AWS.

Responsibilities

Design, build, and maintain production backend services using Python and FastAPI.
Build and extend systems that orchestrate automated evaluations of third-party software products.
Develop long-running background jobs and durable workflows that are resumable, reliable, and observable.
Integrate with third-party APIs and systems, including authentication, rate limiting, retries, partial failures, and changing external contracts.
Build and improve browser-driven automation workflows when APIs are unavailable or insufficient.
Instrument evaluation workflows to ensure results and failures are measurable, reproducible, traceable, and easy to diagnose.
Analyze evaluation results and continuously improve configurations, fidelity, and coverage.
Design solutions that are scalable, maintainable, observable, and production ready.
Build, test, and maintain containerized applications deployed through established CI/CD and Kubernetes environments.
Use AI-assisted and agentic software engineering tools throughout planning, development, testing, and maintenance.
Take ambiguous technical requirements and independently develop practical, defensible implementation approaches.
Identify when existing technical patterns are insufficient and propose new solutions when appropriate.
Collaborate with data science, engineering, and product stakeholders to ensure implementations accurately exercise required product capabilities.
Contribute engineering perspectives to technical discovery, scoping, and prioritization.

Technical Requirements

Strong professional experience building production backend applications with Python.
Hands-on experience with FastAPI or a comparable modern Python web framework.
Experience building and maintaining background job systems, workflow orchestration, or durable execution systems such as DBOS, Temporal, Airflow, Celery, Prefect, AWS Step Functions, or similar.
Strong understanding of idempotency, retries, failure recovery, and long-running stateful workflows.
Proven experience integrating with third-party APIs and external systems, including authentication and resilience to upstream failures.
Experience designing, testing, and operating production-quality distributed or backend systems.
Working knowledge of Docker and Kubernetes-based deployments.
Ability to understand and work effectively with established CI/CD pipelines.
Strong experience with testing, observability, logging, and distributed tracing.
Ability to work autonomously when requirements are ambiguous and independently drive technical discovery and implementation.
Effective use of AI coding agents and AI-assisted development tools as part of day-to-day software delivery.
Strong software engineering and system design fundamentals.
Strong spoken and written English communication skills.
Ability to collaborate directly with US-based client teams and stakeholders working in US Central Time.

Nice to Have

Experience with browser automation frameworks such as Playwright, Puppeteer, or Selenium.
Experience designing reliable headed and headless browser automation and managing flaky automation workflows.
Exposure to AI or agent evaluation methodologies, including evaluation harnesses, benchmark design, LLM-as-judge approaches, or agent trajectory analysis.
Familiarity with AI evaluation frameworks such as τ²-bench (tau2-bench).
Experience working with DBOS or Temporal for durable workflow execution.
Hands-on experience with Argo CD or other GitOps-based deployment approaches.
Experience deploying applications to Kubernetes environments on AWS.
Experience developing product functionality using frontier AI models, including prompting, context management, tool use, and model reasoning.
Familiarity with the AI agent, LLM, or AI developer tooling ecosystem.
Interest in understanding, evaluating, and improving how agent-based software behaves in real-world scenarios.