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Embedded Machine Learning Jobs in Texas (NOW HIRING)

Bumble Inc.

Senior Machine Learning Engineer

Austin, TX · On-site

$220K - $250K/yr

AI is deeply embedded in how we evolve at Bumble. In this role, you'll independently apply modern machine learning and emerging AI techniques, contributing to scalable systems while ensuring ...

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 ...

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How much do embedded machine learning jobs pay per year?

As of May 29, 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
Embedded Machine Learning Engineer (AI/ML)

Embedded Machine Learning Engineer (AI/ML)

Cirrus Logic

Austin, TX • On-site

Full-time

Posted 22 days ago

Job description

Are you a builder at the frontier of Edge AI/ML, eager to apply your craft to high-impact, real-world problems? Do you thrive in ambiguous spaces where data, silicon, and algorithms intersect? Cirrus Venture Labs (CVL) may be the place for you.
CVL is Cirrus Logic's newly formed technology accelerator, chartered to develop disruptive, scalable, and monetizable innovations that solve endemic industry problems. Our vision is to be a globally recognized innovation engine that repeatedly shapes and transforms semiconductor markets. We do so by embedding intelligence directly where signals originate in the physical world, across Voice, Sense, and Control domains, and pioneering ML-augmented signal processing.
As a Principal ML Engineer, you will be a hands-on technical leader shaping CVL's machine learning programs. You will drive the development of ML models, frameworks, and prototyping pipelines, spanning data generation, curation, model engineering, and optimization for deployment on Edge and mixed-signal systems. Partnering closely with Innovation Managers, Architects, external ventures, and away-team contributors, you will turn ambitious hypotheses into validated prototypes that can be scaled into new product categories for Cirrus Logic.
Responsibilites
  • Prototype Development: Lead rapid prototyping of ML models for edge intelligence across Voice, Sense, and Control domains, tightly integrated with Cirrus Logic's mixed-signal processing strengths.
  • Data & Model Engineering: Build datasets, design model architectures, and optimize performance, efficiency, and interpretability. Explore advanced approaches in ML-augmented signal processing, anomaly detection, and adaptive control.
  • System Integration: Collaborate with silicon, firmware, and systems teams to co-design ML architectures that operate efficiently on constrained hardware and embedded systems, balancing algorithmic accuracy with compute and power budgets.
  • Exploration & Research: Stay at the forefront of ML frameworks, foundation/SLM trends, and physical-world AI applications. Scout external IP, academic work, and startups to inform CVL's ML strategy.
  • Mentorship & Technical Leadership: Provide guidance and technical direction to away-team engineers and contributors across Cirrus Logic. Share best practices in ML model lifecycle, from experimentation to deployment.
  • Cross-Functional Collaboration: Work hand-in-hand with Innovation Managers, advisory teams, customers, and external partners to identify opportunities, define success criteria, and validate ML-enabled innovations in real-world scenarios.
  • Impact Assessment: Help define benchmarks, evaluation metrics, and pass/fail criteria that ensure ML prototypes address significant industry problems with clear paths to monetization.

Required Skills and Qualifications
  • Educational Background: Master's or Ph.D. in Computer Science, Electrical Engineering, or related field with a focus on ML/AI.
  • Experience: 8+ years of hands-on experience developing and deploying ML systems on the Edge and within embedded platforms, including ownership of datasets, model development, and deployment pipelines. Proven experience implementing ML inference on resource-constrained systems such as microcontrollers, embedded SoCs, or custom silicon.
  • Architectural Expertise: Demonstrated experience with CNNs, RNNs (LSTM/GRU), and Transformer-based models, including custom architecture design and optimization for production. Experience tailoring these architectures for low-latency and low-power embedded inference.
  • Technical Depth: Strong understanding of representation learning, attention mechanisms, sequence-to-sequence modeling, and generative architectures. Ability to translate these methods into efficient implementations suited for real-time sensor, audio, or control workloads.
  • Optimization for Edge: Experience with quantization, pruning, knowledge distillation, mixed-precision training, and compiler-level optimizations to deploy models on CPUs, DSPs, NPUs, or hybrid SoC architectures. Familiarity with memory hierarchy tradeoffs, compute-offload, and bandwidth constraints in embedded ML.
  • Embedded & Firmware Integration: Proficiency in embedded software and firmware development (C/C++/Python) with experience integrating ML inference engines into real-time embedded stacks, RTOS environments, or bare-metal systems. Understanding of firmware pipelines, peripheral I/O, and signal-path integration for ML-augmented mixed-signal systems.
  • Data Engineering: Ability to design labeling strategies, synthetic data generation, and augmentation pipelines to support robust model development. Understanding of data acquisition and preprocessing directly from embedded sensors.
  • Systems Thinking: Proven track record of co-designing ML and firmware solutions alongside hardware teams, balancing algorithmic, architectural, and physical constraints. Familiarity with embedded ML frameworks and toolchains (e.g., TensorRT, ONNX Runtime, TVM, CoreML, TFLite, Glow, Edge Impulse).
  • Collaboration & Communication: Ability to translate complex ML concepts into actionable insights for cross-disciplinary teams of algorithm, firmware, and hardware engineers.

Preferred Skills and Qualifications
  • Startup & Incubator Experience: Background in early-stage, high-ambiguity environments; experience contributing to incubation of new products or platforms.
  • Specialized ML Expertise: Experience in one or more of: generative models for voice, time-series/sequence modeling, anomaly detection for sensors, reinforcement learning for control systems.
  • Tooling: Familiarity with MLOps frameworks, data labeling pipelines, and distributed training.
  • External Engagement: Experience collaborating with startups, academic labs, or open-source communities.
  • Business Acumen: Ability to assess the business and monetization value of ML solutions in emerging markets.

Join our team and help drive the next wave of foundational technologies that extend the capabilities of Cirrus Logic. If you're passionate about exploring uncharted technological frontiers and delivering disruptive innovations, we'd love to hear from you!
Cirrus Logic is a leading supplier of low-power, high-precision mixed-signal processing solutions for mobile and consumer applications. The company has a robust portfolio of sophisticated low-power products including boosted amplifiers, smart codecs, camera controllers, haptic driver and sensing solutions, power conversion and control ICs, and fast-charging ICs. These solutions have innovative technology, software and associated algorithms incorporated. With a strong intellectual property portfolio and extensive mixed-signal expertise, Cirrus Logic is well-positioned to drive innovation and growth in the evolving markets for audio and high-performance mixed-signal processing technologies.
Cirrus Logic strives to select the best qualified applicant for any opening. Different approaches, ideas and points of view are both valued and respected. Employment decisions are made on the basis of job-related criteria without regard to race, color, religion, sex, national origin, age, protected veteran or disabled status, genetic information, or any other classification protected by applicable law.
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