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.