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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer - **Company:** Recruiting from Scratch - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $130,000.0 - $190,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Code Review, Databases, Continuous Integration, Relational Databases, Software Debugging, Python (Programming Language), PostgreSQL, Machine Learning, MySQL, Node.Js, Software Architecture, Software Deployment, Software Engineering, Systems Integration, TypeScript, Speech Recognition, Core Voice Platform, Google Cloud, Chatbots, Genesys, ReactJS, Delivery Pipeline, Large Language Models, Prompt Engineering, Reliability of Systems, Backend, Kubernetes, Low Latency, Build Tools, Speech Synthesis, Api Design - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-san-francisco-ca--1d1ac7e0-6285-4a58-9b32-4288acb4db02 ## About the Role Software Engineering, Software Engineering, Funding, Process Improvement, Customer Relations, Artificial Intelligence (AI), Fortune 500 Customers, Customer Service Operations, Automation, Artificial Intelligence (AI) Agents, Must have valid work authorization in the United States or Canada. New visa sponsorship and visa transfers are not supported., The Senior Software Engineer role is suited to experienced full-stack engineers who have shipped scalable software, worked hands-on with large language models, and thrive in fast-moving startup environments. The ideal candidate combines strong software engineering fundamentals with practical experience building LLM-powered products, conversational AI, or multi-turn agents., * 5-8 years of experience as a full-stack software engineer. * Demonstrated experience operating at a senior software engineering level. * Experience owning and shipping software solutions that scale to millions of users or support substantial production workloads. * Hands-on experience building products or features that use LLMs. * Experience working in a startup, high-growth technology company, or similarly fast-moving engineering environment. * Strong ownership of projects from design through deployment and ongoing improvement. * Ability to work independently, manage multiple initiatives, and adapt to changing priorities. Education Requirements * No specific degree or university requirement has been provided for this role. * Prioritize demonstrated engineering ability, shipped production software, and practical LLM experience over educational credentials alone. Full-Stack Engineering Experience * Strong proficiency in Python or TypeScript; at least one is required. * Experience with full-stack application development and production software systems. * Familiarity with React and Node.js is relevant for candidates with a TypeScript background. * Experience building APIs, backend services, and user-facing product features. * Ability to make sound technical decisions and deliver maintainable software. * Experience taking features from concept to production rather than working exclusively on isolated components. LLM and Conversational AI Experience * Hands-on experience building or integrating LLM-powered products. * Familiarity with prompt engineering, multi-turn agents, or LLM orchestration tools such as LangChain. * Ability to explain how LLMs are used in a real product or production workflow. * Experience addressing practical challenges such as reliability, latency, response quality, and cost. * Conversational AI, voice AI, or multi-turn agent experience is a strong advantage. * Candidates should demonstrate applied experience rather than theoretical familiarity with LLMs alone. Scalability and Production Engineering * Experience owning and shipping software that serves millions of users or supports high-volume production workloads. * Ability to diagnose and resolve production issues. * Understanding of performance, reliability, scalability, and system design. * Familiarity with relational databases such as MySQL or PostgreSQL. * Experience with cloud platforms such as AWS, Google Cloud, or Azure. * Familiarity with CI/CD pipelines and modern deployment practices. * Ability to balance engineering quality with the need to ship useful product improvements quickly. Ownership and Problem-Solving * Strong initiative and a proactive approach to engineering challenges. * Comfortable taking ownership of ambiguous projects and driving them to completion. * Able to work across the stack and contribute outside a narrowly defined specialty. * Uses sound judgment when choosing between different technical approaches. * Demonstrates curiosity, adaptability, and a willingness to learn. * Can reflect on mistakes and feedback and apply those lessons to future work. Collaboration and Culture Fit * Works effectively with engineering, product, and machine learning teams. * Communicates technical decisions and trade-offs clearly. * Gives and receives code review feedback constructively. * Demonstrates self-awareness and a willingness to improve. * Takes accountability for results rather than focusing only on assigned tasks. * Comfortable in a lean organization where engineers own multiple initiatives., * Experience at companies specializing in conversational AI, voice AI, or enterprise contact-center technology. * Experience at Poly, Decagon, Sierra, Five9, Genesys, ElevenLabs, NICE, Talkdesk, or RingCentral. * Experience with multi-turn conversational agents or LangChain. * Experience integrating speech recognition and text-to-speech technologies. * Experience with Rust. * Familiarity with Google Cloud Services and Kubernetes. * Experience with MySQL, PostgreSQL, and cloud infrastructure. * Experience improving CI/CD pipelines and production deployment workflows. * Experience in a forward-deployed engineering role or working directly on customer-specific technical challenges. * Experience building AI products used by enterprise customers. ## Description * Build user-facing and backend capabilities that improve the functionality, reliability, and scalability of the platform. * Work with TypeScript, React, Node.js, Python, and related technologies. * Own engineering projects from initial problem definition through implementation, testing, deployment, and iteration. * Contribute to product capabilities that help meet customer needs and support business growth. 2. Develop and Enhance LLM-Powered Products * * Integrate LLM capabilities into production systems and customer-facing workflows. * Work with prompt engineering, multi-turn agents, tool use, and conversational AI patterns. * Experiment with new approaches to improve the quality, reliability, and effectiveness of AI-driven interactions. * Collaborate with product and machine learning teams to turn model capabilities into practical product features. 3. Improve Voice and Conversational AI Systems * Help integrate and improve speech recognition, text-to-speech, and conversational AI capabilities. * Build systems that support natural, reliable, multi-turn conversations. * Investigate challenges related to conversation quality, response latency, system reliability, and operating costs. * Debug production issues and improve the performance of AI-powered voice and chat experiences. * Contribute to technology that supports millions of customer interactions for enterprise clients. 4. Own Production Systems at Scale * Build and maintain software designed to support large-scale, enterprise production workloads. * Identify bottlenecks and improve system performance, reliability, and scalability. * Balance latency, cost, reliability, and user experience when making technical decisions. * Investigate complex production issues and implement durable solutions. * Apply sound engineering judgment when evolving systems that are already used by enterprise customers. 5. Collaborate Across Engineering Teams * Work with engineers across multiple product teams to deliver important product capabilities. * Partner with Engineering Managers, Product, and Machine Learning teams to prioritize and execute projects. * Contribute to core product development or forward-deployed engineering initiatives, depending on team needs. * Share technical knowledge, review code, and contribute to engineering standards. * Take ownership of multiple initiatives in a lean organization where engineers have broad responsibilities. 6. Contribute to Forward-Deployed Engineering Initiatives * Work on customer-driven engineering projects when needed. * Translate real-world customer requirements into practical technical solutions. * Build or adapt product capabilities to address complex customer use cases. * Collaborate with customer-facing and internal teams to resolve implementation challenges. * Bring insights from customer deployments back into the core product. 7. Improve Engineering Practices and Systems * Write maintainable, testable, and production-ready code. * Participate in code reviews and help maintain high engineering standards. * Contribute to system design, technical decision-making, and architectural improvements. * Work with cloud infrastructure, databases, deployment workflows, and CI/CD pipelines. * Identify opportunities to simplify systems and improve engineering efficiency. 8. Take Ownership in a High-Growth Environment * Independently drive projects and make progress without requiring constant direction. * Move between product development, debugging, system design, and cross-functional collaboration as needed. * Learn unfamiliar technologies and domains quickly. * Reflect on feedback, identify areas for improvement, and continuously develop engineering skills. * Contribute to a culture of initiative, accountability, and broad technical ownership.