Principal Software Engineer, Breeze

Accountabilities

• Define scalable architecture and technical patterns for AI agents interacting safely with CRM data, agentic tools, memory, scheduled prompts, and third-party ecosystems.
• Establish the right balance between deterministic and non-deterministic workflows, optimizing for reliability, cost, trust, and outcome quality.
• Extend the AI platform to support new artifact types, internal builders, and high-value customer workflows across the broader product ecosystem.
• Partner with context and data-focused teams to ensure AI experiences are powered by complete, accurate, and actionable customer information.
• Rapidly prototype and validate emerging generative AI capabilities, including LLMs, RAG, and agentic frameworks, while determining when solutions are ready for production.
• Write and ship production-quality code while leading high-impact initiatives from early prototypes through reliable systems operating at scale.
• Establish strategies for AI evaluation, observability, privacy, guardrails, and reliability so customers can confidently use AI for meaningful business processes.
• Collaborate with Product, AI Research, Engineering, and Ecosystem teams to make internal tools and APIs accessible through the AI platform.
• Develop reusable platform primitives such as skills, tools, APIs, and agentic infrastructure that other teams can build upon.
• Mentor senior engineers and raise organizational fluency in prompt engineering, model selection, agentic architectures, and production AI systems.
• Advocate for customer needs and ensure technical decisions translate into valuable experiences for sales and marketing professionals.
• Drive technical direction in an ambiguous, rapidly changing environment while maintaining a strong bias toward action and measurable outcomes.

Requirements

• Proven experience designing and scaling high-throughput, low-latency distributed systems with demanding reliability and concurrency requirements.
• Deep hands-on experience with generative AI technologies, including LLM orchestration, vector databases, RAG pipelines, and multi-agent frameworks.
• Strong understanding of model architecture trade-offs, fine-tuning approaches, inference costs, and practical considerations for deploying AI at scale.
• Demonstrated experience designing autonomous or semi-autonomous systems involving tool calling, complex state management, action execution, and graceful failure recovery.
• Strong platform-engineering mindset, with experience building reusable primitives, APIs, tools, or frameworks for other engineering teams and partners.
• Track record of delivering customer-focused products and balancing technical decisions with user experience and business outcomes.
• Ability to turn ambiguous problems into clear plans, working prototypes, and production-ready software without excessive process overhead.
• Curiosity and adaptability in a rapidly evolving AI landscape, with the ability to evaluate new models, research, and frameworks critically.
• Strong communication and technical leadership skills, with the ability to influence cross-functional stakeholders and mentor experienced engineers.
• Willingness to work hands-on while also shaping long-term architecture, engineering standards, and platform strategy.
• Experience working with AI systems where reliability, privacy, evaluation, observability, and trust are critical.
• Proficient written and spoken English and professional experience with an object-oriented programming language.
• Ability to travel as required, including participation in regional in-person onboarding and team events.

Benefits

• Annual cash compensation range of $266,200–$425,900 USD, with individualized packages based on skills, experience, qualifications, and other job-related factors.
• Potential eligibility for equity compensation through restricted stock units (RSUs), depending on the role and compensation package.
• Flexible work options with a remote-friendly environment and opportunities to build strong connections with colleagues.
• Regional in-person onboarding for engineering employees, plus additional opportunities for team and product gatherings.
• Comprehensive benefits and perks designed to support employees' professional and personal well-being.
• Opportunities to work on large-scale AI products with significant customer impact.
• A culture centered on customer focus, bold thinking, rapid learning, adaptability, ownership, and meaningful results.
• An environment that emphasizes trust, flexibility, clarity, and high-quality work rather than traditional office presence.
• Support for workplace accommodations during the recruitment and employment process.