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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 ...

Symbotic

Senior Embedded Software Engineer

Austin, TX · On-site

$122.90K - $161.10K/yr

As a Senior Embedded Software Engineer, you will play a key role in designing, developing, and ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

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$65.2K

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How much do embedded machine learning jobs pay per year?

As of May 30, 2026, the average yearly pay for embedded machine learning in Texas is $142,900.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $161,200.00 per year, depending on experience, location, and employer.

What is an Embedded Machine Learning job?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in the Embedded Machine Learning position, and why are they important?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.
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Embedded Machine Learning jobs near you

Sr. Machine Learning Engineer

AppFolio

Richardson, TX • Remote

$94.30K - $129.50K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

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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