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# Software Engineer (AI/ML)

- **Company:** MIA LABS, INC.
- **Location:** Austin, United States
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Microsoft Azure, Language Modeling, Open Source Technology, Software Engineering, Data Streaming, Speech Recognition, WebRTC, Core Voice Platform, Datadog, Large Language Models, Prompt Engineering, Model Validation, Low Latency, Production Code, Speech Synthesis
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/software-engineer-ai-ml-mia-labs-inc-8722055

## About the Role

* 1+ years of experience in software development (including relevant internships).
 * Strong engineering fundamentals: you write production code, instrument it, and own what you ship
 * Comfort with observability tooling (OpenTelemetry or similar) and making decisions from metrics
 * Scrappy, startup-ready mindset: small team, broad ownership, fast iteration
 * Clear communicator - you'll explain AI behavior and tradeoffs to product, support, and customer-facing teams

Nice to Have

 * Experience with real-time audio or voice AI (STT/TTS pipelines, streaming, WebRTC/networking, telephony)
 * Hands-on experience building with LLMs in production: prompt design, model evaluation, latency/cost optimization
 * Background in speech/audio ML (a founder or early builder of a voice product is a great signal - that's how our current Head of AI got here)
 * Azure cloud experience

## Description

We're hiring a Software Engineer (AI/ML) to work on our voice AI platform, the real-time pipeline of speech-to-text, LLM reasoning, and text-to-speech that powers every call. You'll work on latency, model quality, cost, and reliability across the full AI stack, and help shape which models and vendors we bet on. This is a deep, focused role on a small core product team, with direct mentorship from our AI lead., * Build and improve the real-time voice AI pipeline: speech-to-text, conversational LLM orchestration, and text-to-speech, with hard latency targets (we hold P90 end-to-end latency under 2 seconds and are pushing lower).

 * Improve model performance in production, run evals for core functionality
 * Improve instrumentation (OTel/Braintrust), build dashboards and alerting
 * Optimize cost: squeezing more throughput out of open source LLMs
 * Work with vendors like Anthropic, Microsoft/Azure OpenAI, Deepgram, and NVIDIA - testing new voice and language models, evaluating alternatives, and handling failover when a provider has issues.
 * Ship AI R&D features into production: things like backchanneling, emotional expressiveness, background audio, voice cloning, and multilingual features
 * Implement AI guardrails so models behave correctly on live customer calls


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