Technical Lead Manager

Location

This is a hybrid role based in Palo Alto, with team members expected to work onsite one day per week for in-person collaboration. 

About the role

AI is reshaping how we serve homeowners at Point — from voice agents to email, chat, and SMS agents that enable self-service at scale. We're hiring a Technical Lead Manager to lead the team building them. You'll run a small, senior team, stay hands-on in the code, set technical direction, and work directly with Product, Ops, and CX. If you want to ship applied AI that customers directly interact with, this is the role.

Check out our latest Engineering Blog to learn more about why it's a great time to join Point's Engineering team!

Your responsibilities

• Define the AI technical strategy for CX, including evaluating and selecting frameworks, platforms, and vendor solutions.
• Own the technical roadmap for Point's AI-powered customer experience — voice, email, chat, and SMS agents that handle real homeowner interactions.
• Write code alongside your team — review PRs, prototype new agent capabilities, and debug production issues.
• Partner with Product, CX, and Ops to identify where AI agents can replace or augment manual workflows, then ship it.
• Establish success metrics for agent performance and use data to prioritize the roadmap.
• Design reliable agent architectures — orchestration, tool use, guardrails, fallback handling — that work at production scale.
• Lead and grow a small, senior engineering team: set high expectations, remove blockers, create space for deep work.
• Drive observability and reliability across the AI agent stack, leveraging tools like Langfuse.

About you

• 7+ years of software engineering experience building and operating production systems, including at least 2 years leading or managing engineering teams.
• Proven experience developing and shipping LLM-powered, conversational AI, or agentic systems in real-world environments.
• Track record of delivering customer-facing products end-to-end, from initial design through deployment, monitoring, and iteration.
• Strong ability to partner with cross-functional stakeholders (Product, Operations, CX) to translate business needs into scalable technical solutions and deliver measurable impact.
• Deep systems design expertise, with a focus on building reliable, observable, real-time services.
• Demonstrated technical leadership, including guiding architecture decisions, evaluating trade-offs, and ensuring high-quality execution.
• Experience defining and driving technical strategy and roadmaps for AI-driven, agentic customer experiences aligned with business objectives.
• Proven ability to hire, mentor, and develop high-performing engineering teams, fostering a culture of ownership, innovation, and continuous learning.
• Experience managing and mentoring engineers, driving performance, and building a strong, accountable engineering culture.
• Hands-on experience with LLM and agent development, including prompt engineering, RAG, tool usage, and orchestration frameworks.
• Comfortable making build-vs-buy decisions and rigorously evaluating third-party vendors and platforms.
• Familiarity with conversational AI infrastructure such as telephony, messaging APIs, or chat platforms at scale (preferred).
• Experience with AI/ML evaluation and observability, including instrumentation for quality, drift detection, and continuous improvement (preferred).
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Compensation at Point will be determined by skills, experience, and geographic location. Point has identified the expected annual base salary for this role in the San Francisco Bay Area | $234,650 - $259,350

This does not include any other potential components of the compensation package, including equity, benefits, and perks outlined above. At the launch of each position, we benchmark compensation to the appropriate role and level utilizing competitive compensation data from various data sources as references. At the offer stage, we use the signal we received from our interviews, coupled with your experience, location, and other job-related factors, to determine final compensation.