Software Engineer- Voice AI (Inference Runtime)

Details

• Develop world-class model serving stack for state-of-the-art open-source voice models - reduce end-to-end and tail latency (p95/p99), increase throughput, and improve GPU efficiency via profiling, runtime tuning, and server-level optimizations.
• Build large-scale, real-time infrastructure for multi-model voice agents - orchestrate STT, TTS, and agent components with streaming I/O to meet customer SLOs.
• Design tight training and inference iteration loops for voice model customization - enable fast evaluation, safe rollout, and rapid experimentation for custom voice model development.
• Past projects:The world's fastest Whisper — with streaming and diarizationCanopy Labs selects Baseten for Orpheus TTS inference
• The world's fastest Whisper — with streaming and diarization
• Canopy Labs selects Baseten for Orpheus TTS inference
• The world's fastest Whisper — with streaming and diarization
• Canopy Labs selects Baseten for Orpheus TTS inference
• Own and lead Voice AI product areas end-to-end - from architecture and system design through implementation, rollout, and long-term production operations.
• Design, build, and operate real-time, large-scale, high-performance model serving systems for STT, TTS, and voice agent workloads for mission-critical customer deployments
• Drive cross-team collaboration with sister engineering teams to solve full-stack technical problems, align on priorities, and coordinate end-to-end delivery across the product surface area
• Mentor teammates through code reviews, design docs, and technical leadership.
• Bachelor's degree or higher in Computer Science or related field
• Proven track record owning production-grade real-time, large-scale systems where tail latency (p99) matters.
• Proficient coding abilities in one or more popular programming or scripting languages; Python proficiency is a plus.
• Good taste in product, particularly developer-oriented tools
• Interest in ML/AI infrastructure and willingness to learn
• Strong collaboration and communication skills
• Comfortable using AI coding assistants (e.g., Claude Code, Codex, Cursor) as a daily productivity multiplier — as an AI-native company, we see this as a must-have skill.
• Experience implementing pipeline-level model runtime optimizations such as dynamic batching, async scheduling, or decode-side throughput improvements.
• Experience building developer platforms: SDKs, CLIs, APIs, and self-serve workflows for ML or infrastructure products.
• Experience with containerization and orchestration technologies (Docker, Kubernetes), service meshes, or distributed scheduling.
• Familiarity with speech/audio ML models (STT, TTS, speech-to-speech)
• Familiarity with model-serving runtimes (vLLM, TensorRT, ONNX).
• Familiarity with systems-level performance profiling across host-device boundaries (e.g. PyTorch Profiler), diagnosing GPU utilization issues
• Exposure to customer-facing engineering: pre-sales prototyping, technical discovery, or working directly with customers to ship solutions.
• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
• Paid parental leave
• Fertility and family-building stipend through Carrot
• Company-facilitated 401(k)
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.