[Remote] Staff Platform Engineer, Voice AI

Note: The job is a remote job and is open to candidates in USA. Together AI is a research-driven artificial intelligence company focused on building the next generation of voice applications. They are seeking a Staff Platform Engineer to own the architecture of their Voice AI platform, ensuring reliability and performance for real-time voice agents at scale.ResponsibilitiesOwn the architecture and reliability of Together's real-time API layer — set the technical direction for WebSocket and HTTP streaming APIs powering STT and TTS at scale; establish the reliability bar (connection lifecycle, backpressure, graceful degradation, reconnection) that production voice agents — contact centers, AI agents, communication platforms — depend onLead autoscaling architecture for latency-sensitive voice workloads — design and ship orchestration systems that handle bursty, real-time traffic across tens of thousands of GPUs; solve the hard problems at the intersection of concurrent connection limits, streaming state, and hard latency ceilings that generic autoscalers weren't built forDefine the voice API feature surface — make the architectural calls on word-level alignment, real-time speaker diarization, audio format support (g711/mulaw, PCM, WebRTC), pronunciation controls, and multi-context WebSocket — with a clear view of what unlocks the next category of developer use casesBuild the observability platform for voice infrastructure — design the latency breakdown pipelines, audio quality signal collection, and customer-facing dashboards that give both the team and developers the instrumentation they need to operate at production quality; make debugging voice issues fast and systematicOwn the multi-provider abstraction layer — architect the normalization layer across model partners (Cartesia, Deepgram, Rime, and others) that delivers consistent, provider-agnostic API behavior; your design should absorb upstream variability without exposing it to developersDrive the interface between API and ML serving — partner closely with ML engineering leadership to define the contract between the API layer and the model serving stack; your decisions here have direct impact on end-to-end latency and reliability SLAsRaise the bar for developer experience across the platform — lead API design reviews, shape documentation strategy, define integration patterns and cookbooks; the voice developer experience should be something the industry references, not just adequateArchitect for the product surface that doesn't exist yet — build systems with the foresight that they become the foundation for multiple new voice products; your platform decisions should expand what's possible, not constrain itSkills8+ years of experience building large-scale, real-time distributed systems — with clear ownership of systems that carried production traffic at meaningful scale; you can speak to the architectural decisions you made and defend the tradeoffsDeep, battle-tested expertise in real-time streaming infrastructure — WebSocket server architecture, SSE, bidirectional streaming, connection multiplexing, stateful protocol design — you've debugged production failures in these systems and come out with durable architectural improvementsExpert-level TypeScript and Python, with strong proficiency in systems-level thinking; Rust experience is a meaningful advantage at this level given where voice infrastructure is headingSenior distributed systems judgment — load balancing, autoscaling, rate limiting, and traffic shaping for latency-sensitive workloads aren't concepts you reference, they're problems you've solved under pressureDeep Kubernetes expertise — custom autoscalers, resource management, and health checking for stateful, streaming services; you've built Kubernetes automation that handled edge cases the off-the-shelf tooling couldn'tStrong technical leadership — you set direction, influence across teams without authority, bring clarity to ambiguous problems, and leave systems and teams meaningfully better than you found themSharp product intuition for developer platforms — you have genuine opinions about API ergonomics, you think from the developer's perspective first, and you've shipped tooling that developers actually praisedProven ability to operate with autonomy on high-ambiguity, high-stakes problems — you define the right problem before optimizing the solution, and you've done it on teams where the roadmap wasn't handed to youBachelor's or Master's in Computer Science, Computer Engineering, or related field — or equivalent depth demonstrated through your workExperience with audio and media protocols (WebRTC, g711, PCM encoding) is strongly preferred at this level — the domain specificity mattersFamiliarity with ML model serving infrastructure and how inference engines work is a significant advantage — you'll be a key partner to the ML engineering side of the teamFull-stack experience (React, Next.js) for developer-facing tooling contributions is a plusBenefitsStartup equityHealth insuranceOther competitive benefitsCompany OverviewTogether AI is a cloud-based platform designed for constructing open-source generative AI and infrastructure for developing AI models. It was founded in 2022, and is headquartered in San Francisco, California, USA, with a workforce of 201-500 employees. Its website is https://www.together.ai.Company H1B SponsorshipTogether AI has a track record of offering H1B sponsorships, with 8 in 2026, 19 in 2025, 6 in 2024, 3 in 2023. Please note that this does not guarantee sponsorship for this specific role.