Senior Engineering Manager - Voice AI

We're a remote-first company that values flexibility, curiosity, and collaboration. If you're excited about using AI to solve real problems for real people, Gladly offers a unique environment where advances in AI connect directly to products driving customer impact.

Our mission is to deliver natural, responsive, and reliable AI-powered voice conversations that match or exceed human agent quality — through the right inference infrastructure, latency optimization, and telephony platform.

This team builds the voice intelligence layer of the platform: real-time response generation, inference orchestration, and voice system integrations. We're solving the hardest user experience problems in conversational AI — making sub-second LLM responses sound natural, keeping conversations flowing during inference latency, and ensuring every AI response is accurate and safe.

This is not just another AI job.

As the Engineering Manager for AI Voice, you'll lead the team that makes AI phone conversations feel human. You'll architect systems that hide LLM latency, leverage inference providers for reliability and speed, and build closed-loop feedback systems to make AI responses trustworthy.

We're not chasing hype — we're building a production-grade voice AI platform handling real customer service calls. This is your chance to have a huge technical and product impact, shaping how AI speaks to millions of customers at scale.

• Lead a product team building the voice AI stack: real-time inference, latency management, continuous quality monitoring and performance tracking.
• Collaborate closely with Product, Design, AI Core and AI Research teams to define what "great AI voice experience" means — from first greeting to complex issue resolution.
• Drive innovation — stay on the cutting edge of low-latency LLMs, voice-optimized models, and real-time AI architectures.
• Develop and implement engineering processes and best practices.
• Take ownership of projects throughout their full cycle including inception, planning, execution, testing and maintenance.
• Mentor engineers and foster a culture of quality obsession, AI advancement, latency minimization, reliability, and thoughtful system design.

• Engineering leadership experience: 5+ years leading teams delivering real-time, latency-sensitive systems at scale.
• Previous experience as a top performing software engineer at an AI or SaaS start-up.
• LLM infrastructure knowledge: Experience with inference providers, model orchestration, and managing AI backends in production.
• Technical depth: Strong foundation in backend systems, streaming architectures, and low-latency design patterns.
• Telephony platform knowledge: Experience working with Twilio and WebRTC.
• Bias for action: Proven ability to take ambiguous, early-stage ideas to production.
• Experience ensuring that all of your engineers with variable backgrounds, seniority and understanding of the domain advance in their career development.
• Collaborative leadership: Excellent communication skills — you mentor engineers and bridge between AI research, infrastructure, and product disciplines.

• Hands-on experience with low-latency inference providers, or optimizing LLM response times.
• Voice or conversational AI expertise: latency hiding, utterance acknowledgement, barge-in handling, and natural conversation flow.
• Familiarity with observability tools (DataDog, LangSmith) for monitoring real-time AI performance.
• Experience with provider fallback patterns and resilient architectures.
• Open-source contributions, public talks, or thought leadership in voice AI or real-time systems.

Compensation:

$183,000 to $215,000 annual salary + equity + benefits

For cash compensation, we set standard ranges for all U.S.-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.