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Machine Learning Engineer Jobs in McKinney, TX (NOW HIRING)

AppFolio

Sr. Machine Learning Engineer

Richardson, TX · On-site

$94K - $129K/yr

Who We Are Looking For 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 ...

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Machine Learning Engineer information

See McKinney, TX salary details

$29.2K

$119.5K

$179.6K

How much do machine learning engineer jobs pay per year?

As of Aug 7, 2026, the average yearly pay for machine learning engineer in McKinney, TX is $119,502.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,200.00 and $143,800.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in McKinney, TX? The most popular types of Machine Learning Engineer jobs in McKinney, TX are:
What are popular job titles related to Machine Learning Engineer jobs in McKinney, TX? For Machine Learning Engineer jobs in McKinney, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in McKinney, TX look for? The top searched job categories for Machine Learning Engineer jobs in McKinney, TX are:
What cities near McKinney, TX are hiring for Machine Learning Engineer jobs? Cities near McKinney, TX with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in McKinney, TX as of August 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 79% In-person, and 21% Remote job distribution, with an average salary of $119,502 per year, or $57.5 per hour.

Sr. Machine Learning Engineer

AppFolio

Richardson, TX • On-site

$94K - $129K/yr

Full-time

Re-posted yesterday

AppFolio rating

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

180th of 242 rated software companies

Job description

Hi, We're AppFolio
We're innovators, changemakers, and collaborators. We're more than just a software company — we're building the AI-native platform where the real estate industry comes to do business. We're transforming Property Management; how property managers operate, how residents live, and how intelligence flows across an entire industry.
Realm-X is AppFolio's AI-native platform powering this transformation. It enables a new generation of intelligent capabilities across our products, including Realm-X Assistant (copilot), Flows (AI Agentic workflows) and Performers (autonomous AI Agents). Realm-X serves as both a foundation for internal teams to build and scale AI-powered products, and a core layer delivering intelligent, high-impact experiences directly to our customers.
At its core, Realm-X is built on a structured domain ontology and a set of shared business primitives—such as transactions, actions, reports, metrics, and skills—that enable AI systems to deeply understand and operate across the full context of property management workflows. This foundation allows us to build context-aware, action-oriented AI systems that go beyond simple assistance to power real automation and decision-making.
Who We Are Looking For
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.
Qualifications
  • 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
  • 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.
Location
Find out more about our locations by visiting our site. 
Compensation & Benefits
The compensation that we reasonably expect to pay for this role is: 167,200 - 209,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate’s skills, education, experience, and internal equity.
Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type.
Regular full-time employees are eligible for benefits - see here.

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