Senior Machine Learning Engineer, Voice Agents

Accountabilities • Take architectural ownership of significant parts of the open-source speech-to-speech library, including pipeline design, latency budgets, and realtime loop reliability.

• Integrate new ASR, TTS, and end-to-end speech models as they become available while maintaining clean, extensible abstractions.

• Review community contributions, triage issues, manage releases, and help grow the contributor community around the project.

• Design the developer API and streaming protocol for the voice platform, including session lifecycle, WebSockets/WebRTC transport, authentication, error semantics, and versioning.

• Build and operate the serving infrastructure for realtime GPU inference, including concurrency, autoscaling, observability, and cost-per-session optimization.

• Collaborate with Hub and inference teams to make voice agents easy to integrate into products, applications, and demonstrations.

• Take the platform from prototype to production through load testing, SLO definition, reliability improvements, and graceful degradation when models or network paths fail.

• Create documentation, examples, and templates that enable developers to move from initial setup to a running voice agent quickly.

• Support deployments already relying on the technology, including the existing robotics fleet.

• Contribute to the wider technical community through blog posts, demonstrations, conference talks, or other public technical content when desired.

Requirements

• Senior-level engineering experience with the ability to independently own a substantial part of an architecture and drive it forward.

• Experience building developer-facing infrastructure in AI, machine learning, developer tools, or a comparable technical environment, such as inference APIs or agent infrastructure.

• Significant open-source contributions to a Python library and strong proficiency with asynchronous Python.

• Solid understanding of distributed systems and their failure modes.

• Proven experience shipping realtime technology involving streaming, WebSockets, WebRTC, audio or video pipelines, or live inference.

• Practical production experience with LLMs or multimodal models.

• Strong written communication skills and a demonstrated ability to collaborate asynchronously and in public.

• Genuine interest in voice technology and conversational AI.

• Contributions to voice-agent frameworks such as speech-to-speech, Pipecat, LiveKit Agents, Vocode, or TEN are a plus.

• Experience contributing to llama.cpp or another low-level inference runtime is advantageous.

• Hands-on experience with ASR, TTS, or end-to-end speech models, including evaluating latency and quality trade-offs, is a plus.

• GPU serving, quantization, or on-device inference experience is advantageous.

• Knowledge of audio pipelines, including VAD, echo cancellation, jitter buffers, barge-in, and turn detection, is a plus.

• Experience deploying technology to embedded or robotics environments is beneficial.

• A public technical track record through talks, blog posts, demos, or similar contributions is valued.

• Candidates are encouraged to apply even if they do not meet every listed requirement, particularly where their experience could bring complementary strengths to the team.

Benefits

• Flexible working hours and remote work options .

• Health, dental, and vision benefits for employees and their dependents.

• Parental leave and flexible paid time off.

• Company equity as part of the compensation package for all employees.

• Reimbursement for relevant conferences, training, and education to support continuous professional development.

• Access to a distributed, international work environment with opportunities to collaborate with experienced professionals across the AI and machine learning community.

• Opportunities for remote employees to visit company offices in New York City and Paris .

• Workstation equipment and setup support when needed to help employees work effectively.

• A strong commitment to diversity, equity, and inclusion , with a workplace designed to ensure employees feel respected and supported.

• Opportunities to contribute to and connect with the broader ML/AI community .

• A culture focused on impact, continuous learning, collaboration, and professional growth.

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