> Markdown version of [/jobs/ext/3806255-sr-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3806255-sr-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr. Machine Learning Engineer

- **Company:** AppFolio, Inc.
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Salary:** $167,200.0 - $229,900.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Amazon Web Services, Software as a Service, Computer Programming, Github, Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Inference Optimization, Azure Machine Learning, Data Streaming, Twilio, WebSocket, LangChain, Retrieval-Augmented Generation, Large Language Models, Deep Learning, Model Validation, Agentic-AI, LangSmith, Information Technology, Low Latency, Docker
- **Published:** October 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=97506527ad4f79b7

## About the Role

* You have 5+ years of experience developing and scaling web-based applications, preferably in a SaaS environment.
 * You have shipped production AI agents serving real users in voice and/or text channels.
 * You think in pipelines and systems, not just models.
 * You move fast, deliver impact, and maintain sound engineering judgment.
 * You are humble, collaborative, and low-ego, and you elevate those around you.
 * You value work-life balance as a foundation for sustained high performance.

Must Have

 * Agent frameworks: Deep, shipped experience with LangChain, LangGraph, LangSmith, and LangChain Deep Agents (or equivalent agent frameworks).
 * Voice stack: Hands-on with Voice-to-Voice models and traditional TTS / STT pipelines; understands the trade-offs between end-to-end voice models and modular STT LLM TTS architectures.
 * LLM fluency: Strong grasp of LLM reasoning behavior, tool use, structured output, and reasoning-vs-latency trade-offs across providers.
 * Telephony & cloud: Production experience with Twilio (or comparable telephony) and AWS.
 * Engineering: Expert Python, async programming, and WebSockets for real-time, bidirectional streaming.
 * ML fundamentals: Solid foundation in deep learning, model evaluation, and inference optimization; able to deploy with Docker on AWS.
 * Leadership: Demonstrated ability to lead a small team, mentor engineers, and partner credibly with Product and Design.

Nice to Have

 * Bachelor's, Master's, or Ph.D. in Computer Science or related technical discipline.
 * Experience fine-tuning Small Language Models for domain-specific voice applications.
 * Familiarity with RAG over structured business data and tool-using agents over API surfaces.
 * Prior experience in regulated or customer-facing industries with strict reliability requirements.
 * Publicly verifiable work on GitHub, in open-source agent frameworks, or in community competitions.

## Description

We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio's production voice and chat agent pipelines, working at the intersection of LLM agent frameworks, real-time voice technology, and streaming infrastructure.

You will work with Product, Voice channel, and ML Platform teams to translate cutting-edge agent and voice research into reliable, low-latency, multi-channel experiences that scale across our entire customer base.

Your Impact

 * Ship Voice & Text Agents: Architect and ship voice and text agent pipelines that handle real-time, multi-turn customer interactions.
 * Reasoning vs. Latency: Make principled trade-offs between reasoning depth and latency across frontier LLMs, smaller models, and routing strategies.
 * Lead a Pod: Lead a small pod of ML and platform engineers; raise the bar on agent evaluation, observability, and incident response.
 * Define Quality: Partner with Product and Voice channel teams to define KPIs, eval harnesses, and acceptance criteria for agent quality.
 * Optimize for Voice: Drive selective Small Language Model (SLM) fine-tuning and inference optimization for voice latency and cost.