[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.

Responsibilities

• Own 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 on
• Lead 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 for
• Define 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 cases
• Build 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 systematic
• Own 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 developers
• Drive 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 SLAs
• Raise 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 adequate
• Architect 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 it

Skills

• 8+ 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 tradeoffs
• Deep, 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 improvements
• Expert-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 heading
• Senior 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 pressure
• Deep 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't
• Strong 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 them
• Sharp 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 praised
• Proven 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 you
• Bachelor's or Master's in Computer Science, Computer Engineering, or related field — or equivalent depth demonstrated through your work
• Experience with audio and media protocols (WebRTC, g711, PCM encoding) is strongly preferred at this level — the domain specificity matters
• Familiarity 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 team
• Full-stack experience (React, Next.js) for developer-facing tooling contributions is a plus

Benefits

• Startup equity
• Health insurance
• Other competitive benefits

Company Overview

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