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Hourly Embedded Machine Learning Jobs in Texas (NOW HIRING)

AppFolio

Sr. Machine Learning Engineer

Richardson, TX · Remote

$94.30K - $129.50K/yr

Assistant: a GenAI copilot embedded across the product experience * Flows: an agentic workflow ... Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in ...

Avride

Machine Learning Engineer Internship

Austin, TX · On-site

During this internship, you will be embedded in our Perception team. The Perception team serves as ... Machine Learning / Math Foundation: Strong understanding of deep learning, reinforcement learning ...

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Hourly Embedded Machine Learning information

What are the key skills and qualifications needed to thrive as an Hourly Embedded Machine Learning Engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

How does an Hourly Embedded Machine Learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What is an Hourly Embedded Machine Learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are the most commonly searched types of Embedded Machine Learning jobs in Texas? The most popular types of Embedded Machine Learning jobs in Texas are:
What are popular job titles related to Hourly Embedded Machine Learning jobs in Texas? For Hourly Embedded Machine Learning jobs in Texas, the most frequently searched job titles are:
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Hourly Embedded Machine Learning jobs near you

Sr. Machine Learning Engineer

AppFolio

Richardson, TX • Remote

$94.30K - $129.50K/yr

Full-time

Posted 22 days ago

Job description

Hi, We’re AppFolio
We’re innovators, changemakers, and collaborators. We’re more than just a software company — we’re building the cloud and AI-native platform where the real estate industry comes to do business. We’re revolutionizing how property managers operate, how residents live, and how intelligence flows through an entire industry.
We are now building the next generation of our platform with AI at the core.
Realm-X is AppFolio’s AI platform powering this transformation:
  • Assistant: a GenAI copilot embedded across the product experience
  • Flows: an agentic workflow system enabling automation of complex business processes
  • Performers: real-time, multi-modal AI agents operating across voice, text, email, and chat
We are building not only these experiences, but also the platform that enables teams across AppFolio to contribute and extend AI capabilities.
At the foundation are deep agents, built on a real estate ontology and domain primitives (transactions, actions, reports, metrics, and skills), allowing AI systems to understand and operate across the full business context of AppFolio — powering both employee productivity and end-to-end automation.
Who we are looking for
We’re seeking a Sr Machine Learning Engineer to play a critical role in shaping Realm-X and the future of AI at AppFolio.
This is a high-impact position focused on defining architecture, building next-generation AI systems, and influencing technical direction across teams. You will work at the intersection of machine learning, distributed systems, and product innovation to create AI systems that move beyond assistance into execution.
Responsibilities:
  • Define and drive the technical vision and architecture for AI systems within Realm-X
  • Design and build deep, context-aware agents leveraging domain ontologies and structured business primitives
  • Lead the development of agentic workflows (Flows) that combine reasoning, planning, and execution
  • Architect systems for real-time, multi-modal AI agents (Performers) across communication channels
  • Build and evolve platform capabilities (tools, memory, evaluation systems, abstractions) to enable broad internal adoption
  • Translate ambiguous, high-impact problems into scalable, production-ready AI systems
  • Establish best practices for LLM evaluation, observability, safety, and iteration loops
  • Collaborate cross-functionally with product, design, and engineering leaders to shape strategy and execution
  • Mentor engineers and raise the technical bar across the organization
  • Identify and introduce emerging AI technologies and paradigms that create leverage for the business
You know you’re the right fit if…
  • You think in terms of systems and platforms, not just features
  • You have a track record of building and deploying ML/AI systems in production at scale
  • You are comfortable operating in high ambiguity and defining direction where none exists
  • You can lead through influence, aligning multiple teams around a technical vision
  • You balance long-term architecture with pragmatic delivery
  • You are motivated by high-impact problems that shape products and business outcomes
Additional Skills and Knowledge:
  • Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field (required)
  • Extensive experience developing and deploying machine learning systems in production environments
  • Strong software engineering expertise with languages such as Python, Go, Ruby, or JavaScript
  • Deep understanding of distributed systems, APIs, and cloud infrastructure (AWS or similar)
  • Experience leading large, cross-functional technical initiatives
  • Ability to design systems that integrate structured data, models, and real-time decisioning
Nice to Have:
  • Experience with LLMs, AI agents, and tool-using systems (e.g., LangChain, LangGraph, OpenAI APIs)
  • Familiarity with agentic architectures, planning/execution loops, and orchestration frameworks
  • Experience building domain-specific ontologies, knowledge graphs, or semantic layers evaluation frameworks for AI systems (offline and online)
  • Background in workflow orchestration systems (e.g., Temporal)
  • Experience building platforms that enable other engineering teams
  • Exposure to multi-modal AI systems (voice, chat, email, etc.)
Compensation & Benefits
The compensation that we reasonably expect to pay for this role is: 167,200.00 - 209,000.00 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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