Staff Software Engineer - Internal Efficiency
New
J
JobgetherSoftware Engineering
Work remotely from within the United States or from the South San Francisco, California headquartersFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonJavaSoftware ArchitectureSpringMicroservicesPrompt Engineering
Requirements
- Bachelor’s degree in Computer Science or a related technical discipline.
- 8+ years of experience developing enterprise or consumer-facing web applications.
- Strong experience in Python or Java/Spring.
- Hands-on experience building and deploying LLM-powered solutions in production environments.
- Experience with agentic tools and frameworks, prompt engineering, context engineering, and AI-assisted development workflows.
- Practical experience with modern AI development tools and models including Claude and Codex.
- Demonstrated ability to work directly with non-engineering stakeholders to uncover requirements and translate ambiguous business problems into production-ready software.
- Proven ability to drive adoption of solutions and demonstrate measurable improvements in efficiency.
- Strong software engineering fundamentals including architecture, development, testing, and maintenance.
- Strong communication skills and ability to explain technical trade-offs to non-technical stakeholders.
- Highly proactive and self-directed with ability to operate with broad ownership.
Responsibilities
- Embed directly with internal teams to understand workflows, map processes, identify bottlenecks, and prioritize high-leverage opportunities for AI-driven efficiency improvements.
- Translate ambiguous operational challenges into clearly defined technical solutions with measurable outcomes and success criteria.
- Own the complete software lifecycle, from discovery, requirements gathering, and rapid prototyping through production deployment, adoption, measurement, and continuous improvement.
- Design, build, deploy, and maintain AI-powered tools, agents, automations, and internal capabilities that improve how teams operate.
- Integrate AI solutions with Python- and Java-based microservices, web and mobile applications, internal systems, and third-party platforms.
- Drive adoption of agentic software development practices across engineering.
- Evaluate emerging AI models, tools, and frameworks through rapid experiments.
- Establish and monitor metrics such as time saved, throughput, quality, cost, and adoption.
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