Baseten
Software Engineer (Voice AI, Inference Runtime)
Skills
2 months ago
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Important information
Contract type:
Permanent
Salary:
$165,000 – $330,000 / year
Location:
United States
Work mode:
Onsite
Published on:
21 October 2026
What they need
- Voice is becoming the internet’s next interface, but a production-grade Voice AI system is “hard to build”
- You’ll join a small founding team of Baseten Voice AI, focused on bringing state-of-the-art open source models into production for Voice AI customers across productivity, customer service, clinical conversation, creator tools, education, and more
- You’ll make a meaningful impact on people’s daily lives and help reshape these industries
- This is a high-impact, high-ownership role
- You will be the primary owner of Baseten Voice AI - our in-house inference stack to power Voice AI models - from product roadmap through engineering implementation
- You’ll partner closely with Forward Deployed Engineers, Model Performance Engineers, and sister engineering teams to push the boundaries of Voice AI
- 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 with clear SLOs for mission-critical customer deployments
- Drive cross-team collaboration with sister engineering teams to solve full-stack technical problems, aligning on priorities, and coordinating end-to-end delivery across the product surface area
- Mentor teammates through code reviews, design docs, and technical leadership
Benefits
Remote-first work environment. The Baseten team is welcome to work from wherever they want; fully remote, in our San Francisco office, or a mix of both. Today, our team (including our founding team) is spread across the United States, Canada, and Armenia. We provide a $1,000 stipend for you to make your home-office comfortable and productive
Regular in-person team summits. We get together as a team three times a year to plan, workshop, and most importantly, get to know each other better
Unlimited PTO. We ask that everyone take at least 4 weeks of vacation. And we have a company-wide break between Christmas and New Year’s Day
Full healthcare coverage. Medical, dental and vision insurance for you and your family
Paid parental leave. 16-weeks fully paid parental leave (adoptive and non-birth parents included) and flexibility with schedules while returning to work
Company-sponsored 401(k) for you to contribute to
Learning and development budget. We encourage you to take classes, attend conferences, and invest in your craft and we’ll cover expenses to make it happen
Strong collaboration and communication skills
Interest in ML/AI infrastructure and willingness to learn
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
Good taste in product, particularly developer-oriented tools
Proficient coding abilities in one or more popular programming or scripting languages; Python proficiency is a plus
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
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
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
Experience with containerization and orchestration technologies (Docker, Kubernetes), service meshes, or distributed scheduling
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