About the job - AI Engineer
Join the Engineering Team, where you’ll help shape and build our agentic future. You’ll work closely with our Product and Engineering Teams, and our Chief Product & Technology Officer (CPTO), to turn vision into reality, partnering with a small, high-impact team to design, prototype, and ship AI-powered capabilities quickly. This role is hands-on and iterative, focused on building production-grade agentic workflows that improve how internal communications are curated, designed, delivered, measured, and orchestrated.
Your impact
- Architectural Ownership: Contribute to the technical vision for our AI layer, driving technology adoption and research initiatives from spike to task breakdown, with a focus on core AI infrastructure to ensure our systems are robust and scalable.
- Agentic Design & Orchestration: Move beyond simple features to architect and implement robust agentic workflows, multi-agent systems, intelligent system behaviors, and orchestration patterns (e.g., dynamic routing, autonomous agents, feedback loops).
- Strategic Execution & Experimentation: Operate with autonomy by building and executing against a 3-month strategic roadmap while refining work as you learn. Design and execute different approaches and experimentation frameworks to benchmark model value, measure success, and systematically iterate on prompts and model configurations.
- Production Ownership & Reliability: Own the reliability and scalability of AI subsystems in production, so our AI layer stays trustworthy as it grows. This includes designing and implementing comprehensive testing strategies that move beyond existing framework, focusing on systematic testing of LLM responses and failure modes.
- Cross-Functional Partnership: Collaborate closely with Product Management and the CPTO to translate business vision into concrete technical specifications and product-facing or internal workflow automation tools.
- Lead by Example: Be active within your team and the company. Participate in meetings; volunteer to lead initiatives; escalate questions or issues when necessary. Act as a subject matter expert for complex subsystem troubleshooting, cross-org dependencies, and architectural reviews, including how to structure and maintain documentation (e.g., using agents, markdown, or external Knowledge Bases).
- Cultural Stewardship: Be a full participant in helping the engineering culture evolve as we grow.
About you
- You hold a Bachelor’s degree (or higher) in Computer Science, Statistics, Mathematics, or Engineering, or equivalent practical experience.
- 7+ years of professional software engineering experience, including 2+ years building AI/ML or data-driven systems in production SaaS environments.
- You possess deep technical proficiency in AI systems, including handling complex subsystem interactions and dependencies.
- You've moved LLM work past prototypes into production—thinking through eval frameworks, monitoring output quality, and iterating on prompts based on real-world performance.
- You understand how to structure LLM calls for reliability: using function calling to ground outputs, testing responses systematically, and designing for failure modes (hallucinations, latency, cost).
- You have working knowledge of multi-agent orchestration frameworks and RAG architectures, including vector stores.
- You prioritize a "question first, then answer" approach, thinking critically about problems before committing to a code path.
- You bring intellectual humility, empathy, and strong listening skills to the team; you lead by example.
- You are able to work collaboratively and independently.
- You are comfortable with agile development.
- You have excellent oral and written communication skills.
How you can stand out
- You have designed and shipped autonomous workflows where LLMs reason across multiple steps, make decisions based on tool outputs, and adapt their behavior based on feedback.
- You have previous experience working within the email ecosystem or similar high-scale communication platforms.
- You have a proven track record of rapid prototyping, iterating quickly on features, and leveraging data to pivot when necessary.
- You act as a catalyst for new engineering principles and tools within your current organization, helping to raise the technical bar for your peers.