Engineering Manager

Job Description

Job Description

ABOUT US:

Founded in 2020, Regal is the AI Agent Platform. Regal gives every company the tools to transform customer communications with delightful AI Agents that are connected to your data, easy to customize and monitor, always available, and ready to take action. Power better support, sales, and operations – with way less effort. Our founders, Alex Levin and Rebecca Greene, helped build Angi (Angie’s List, HomeAdvisor, and Handy) to over $1.5B in revenue. 

 

Based in Manhattan, we’re building an in-person culture of entrepreneurs who want to win and build something meaningful. We’re backed by top investors including Founder Collective, Homebrew, and Emergence Capital.

 

Come join us as we create a category-defining company, and follow Regal's company page on LinkedIn to stay up-to-date on our journey and current job openings!

 

We’re moving fast, and the numbers speak for themselves:

- Partnered with enterprise brands like Google, AAA, Ro, Coursera

- Raised $82M (top tier investors including Emergence & Homebrew)

- Completed 500M+ calls

- Driven $7B revenue for customers

- Scaled to $##M ARR

- Built amazing NYC (Midtown) in office culture

ABOUT THE ROLE:

We're hiring an Engineering Manager to lead our Observe/Evaluation team — the team responsible for making sure our voice agents actually work in production. Your core focus is two-fold: the observability tooling that gives us visibility into how agents behave, and the evaluation frameworks that let us systematically measure and improve agent quality over time. You'll also touch the integrations that connect our systems to the broader ecosystem, and a customer-facing copilot product that lets customers iterate on their own agents easily.

RESPONSIBILITIES:

• Lead and grow a team of 3-5 engineers — set expectations, support career development, and hold a high hiring bar
• Own execution of the observability and evaluation roadmap: tooling that surfaces how voice agents are performing and frameworks that let teams evaluate agent quality systematically
• Stay hands-on enough to jump into Python code, debug production issues, and make tactical contributions when needed
• Partner with the other product teams to translate voice-agent quality signals into tooling and metrics that are actually actionable
• Own the reliability and scalability of the team's integrations — the connections that link our systems to complex external and internal systems, and that need to scale as the business grows
• Support the copilot product's roadmap in collaboration with Product — the tool that lets customers iterate on their voice agents easily
• Remove blockers for your team — whether that means adjusting process, pairing on a hard debugging session, or resolving cross-functional friction
• Be actively involved in hiring: sourcing, interviewing, and onboarding

WHAT YOU'LL OWN:

• Observability tooling : Systems that give visibility into how voice agents are behaving in production — what's working, what's degrading, and why
• Evaluation frameworks : Tooling that lets the org systematically evaluate and improve voice agent quality over time, rather than relying on ad hoc checks
• Integrations : The complex external and internal integrations that connect our systems to the broader ecosystem, built to scale as usage grows
• Copilot product : A customer-facing product that lets customers iterate on their voice agents easily — this is a product we ship, not an internal tool

ABOUT YOU:

• 6+ years of experience in a relevant engineering role
• 1-3 years of experience managing a team
• Hands-on experience with Python and Typescript — you can still write and debug code at a senior level, even if you now do it less often
• Solid experience with AWS, including services like Lambda and SQS, and streaming systems (e.g. Kinesis)
• Comfortable managing ambiguity — this space (observability + eval for AI agents) is still being defined, and you'll help define it
• Strong written and verbal communication skills; able to work with both technical and non-technical stakeholders
• A track record of mentoring engineers and helping them grow

NICE TO HAVE:

• Experience with MCP (Model Context Protocol) or building agentic applications 
• Background in observability, monitoring, or evaluation tooling specifically
• Experience working adjacent to applied AI or ML teams
• Prior startup experience at the Series B–D stage

BENEFITS/PERKS

• We care about your health! 
• Medical, Dental, and Vision plans - 80% covered by the company
• Flexible PTO & 11 paid holidays/year 
• We care about future you!
• 401k Plan
• Paid parental leave
• Pre-tax commuter benefits 
• We care about connection!
• In-office breakfast and snacks daily
• Happy hours, team outings, & annual off-sites
• Complete laptop workstation 
• & more to come!

The reasonably estimated base salary for this role is provided as a range within this job description. All offers include a competitive equity package and some offers may additionally include a variable compensation component. Actual compensation is determined on an individualized basis taking into consideration factors such as the candidate's skills, location, qualifications, experience, and relevant education or training. In addition, Regal offers a comprehensive set of employee benefits which are listed above. Details about the compensation package will be finalized at the time of offer.

POSITION LOCATION & OFFICE DETAILS:

 

This position is only available in New York City (HQ- Midtown). Hybrid roles are required in office T/W/TH and office optional M/F.

 

*If you think you’re missing relevant experience but you’re hungry and a fast learner (and can prove it), we want to hear from you!

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.