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# Senior Software Engineer

- **Company:** Recruiting from Scratch
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $170,000.0 - $311,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Applications, Big Data, Cloud Computing, Databases, Distributed Systems, Elasticsearch, Python (Programming Language), Enterprise Messaging Systems, MongoDB, Ruby on Rails, Ruby, Software Engineering, Systems Architecture, Google Cloud, ReactJS, AI Coding Agents, Backend, Agentic-AI, Event Driven Architecture, Kubernetes, Apache Kafka, Terraform, Docker
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-san-francisco-ca--37fe2603-3408-4a11-983d-8ef822f8778f

## About the Role

Software Engineering, Software Engineering, Sales, Marketing, Artificial Intelligence (AI), Sales Closing Skills, Business-to-Business (B2B), Sales Prospecting, Continuous Improvement, Product Development, * 5-8 years of professional experience in backend or full-stack software engineering.

 * Demonstrated experience building and operating large-scale distributed systems.
 * Strong software engineering fundamentals and the ability to make sound architectural decisions.
 * Experience owning significant systems or features from design through production and ongoing operation.
 * Ability to select and combine technologies to solve complex engineering problems.
 * Strong problem-solving skills and comfort working through ambiguous requirements.
 * A track record of delivering reliable, performant, and maintainable production software.
 * Experience working collaboratively across engineering and product teams.
 * Interest in building AI-native products and agent-driven systems.
 * Willingness to work in a hybrid arrangement, with three days per week in the San Francisco office., * Backend or full-stack software engineering.
 * Distributed systems design and implementation.
 * Production system ownership, reliability, scalability, and performance.
 * Technology selection and architectural trade-offs.

Relevant Technologies

 * Languages and frameworks: Ruby, Ruby on Rails, Python, React.
 * Databases and search: MongoDB, Elasticsearch.
 * Infrastructure and deployment: Kubernetes, Docker, Terraform.
 * Cloud: Google Cloud Platform (GCP).
 * Distributed systems: Experience with Kafka, databases, and comparable technologies is relevant.

Candidates do not need to have worked with every technology listed. Prioritize demonstrated distributed systems expertise, strong engineering judgment, and the ability to learn and combine technologies effectively.

Preferred Background

 * Experience operating distributed systems at significant scale.
 * Experience building backend platforms or complex full-stack products.
 * Experience with event-driven architecture, messaging systems, or Kafka.
 * Experience with databases, search infrastructure, and data-intensive applications.
 * Experience deploying and operating services using containers and cloud infrastructure.
 * Experience with AI-powered applications, agentic systems, or AI-native product development.
 * Experience using AI coding assistants or agents as part of everyday engineering workflows.
 * Experience taking ownership of complex systems with clear accountability for reliability, performance, and cost.

Working Style and Culture

 * Extreme ownership: Takes responsibility for systems from initial design through production operation.
 * Strong engineering judgment: Knows when and how to use different technologies to solve a problem.
 * High technical standards: Cares about reliability, performance, correctness, scalability, and cost.
 * Comfort with ambiguity: Can break down complex problems and drive them toward practical solutions.
 * AI-native mindset: Uses AI-assisted development tools to increase engineering leverage and move faster.
 * Collaboration: Works effectively with product, design, engineering, and leadership.
 * Continuous improvement: Looks for ways to improve systems, workflows, and technical decision-making.
 * Bias toward execution: Moves from investigation and design to shipped outcomes.
 * Adaptability: Can contribute across high-priority projects as business needs evolve.

## Description

* Collaborate across teams: Partner with engineering, product, design, and leadership to deliver high-impact features and solve cross-functional challenges.
 * Use AI-assisted development: Integrate AI coding tools and agents into your daily workflow to accelerate design, investigation, and implementation.
 * Improve production systems: Continuously improve system architecture, scalability, reliability, and performance as the product evolves.
 * Take significant ownership: Work independently on important engineering initiatives while collaborating with a high-bar engineering team.