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Hourly 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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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
Embedded Machine Learning Engineer (AI/ML)

Embedded Machine Learning Engineer (AI/ML)

Cirrus Logic

Austin, TX • On-site

Full-time

Posted 21 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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