Applied AI Engineer
Skills
- Strong Python programming fundamentals and software engineering practices.
- Hands-on experience with LLM tools/APIs (OpenAI, HuggingFace, or similar).
- Familiarity with cloud platforms (GCP or AWS) and containerization (Docker).
- Experience building APIs/services (e.g., FastAPI/REST) and working in Git-based codebases.
- Knowledge of testing practices, CI/CD workflows, and deployment/monitoring basics.
- Understanding of LLM failure modes, safety considerations, and reliability trade-offs.
Responsibilities
- Implement and ship applied AI features from prototype to production, contributing to design, rollout, and ongoing improvements.
- Translate user feedback and research prototypes into clear technical requirements and delivery plans.
- Build LLM- and agent-based workflows including tool-calling agents, structured outputs, retrieval integrations, and safe prompting.
- Write production-grade code with clear abstractions, stable interfaces, and robust error handling.
- Develop and maintain tests (unit, integration, golden/regression) to ensure reliability of AI features.
- Add observability and debugging instrumentation such as logging, tracing, and metrics for AI features.
- Apply security and safety best practices (rate limiting, input validation, prompt-injection awareness).
- Collaborate with researchers and engineers to productionize experiments and document deployment best practices.
Education
- Bachelor's degree in Computer Science, Electrical Engineering, Physics, or related field preferred, or equivalent practical experience.
- Relevant internship experience, open-source contributions, or substantial project work considered.
Benefits
- Competitive compensation
- Stock options plan
- Access to cutting-edge tools and collaboration with experts in AI and hardware
- Opportunities for professional growth including conferences and presentations
- Impact-driven culture working on high-leverage scientific and engineering problems
Average salary estimate
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