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Hourly Embedded Machine Learning Jobs in Saint Louis, MO

Chenega Corporation

ServiceNow Developer

Saint Louis, MO · On-site

$52.50 - $72/hr

Are you looking for a career where professional development is embedded in your employer's core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

Chenega MIOS

ServiceNow Developer

Saint Louis, MO · On-site

$51.25 - $70.50/hr

Are you looking for a career where professional development is embedded in your employers core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

Chenega Corporation

ServiceNow Developer

Saint Louis, MO

$52.50 - $72/hr

Are you looking for a career where professional development is embedded in your employer's core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

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All JobsHourly Embedded Machine Learning JobsHourly Embedded Machine Learning Jobs in Saint Louis, MO

Hourly Embedded Machine Learning information

See Saint Louis, MO salary details

$68.1K

$149.1K

$169.2K

How much do hourly embedded machine learning jobs pay per year?

As of Aug 18, 2026, the average yearly pay for hourly embedded machine learning in Saint Louis, MO is $149,124.00, according to ZipRecruiter salary data. Most workers in this role earn between $127,800.00 and $168,200.00 per year, depending on experience, location, and employer.

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.

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 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.

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 Saint Louis, MO?

The most popular types of Embedded Machine Learning jobs in Saint Louis, MO are:

What are popular job titles related to Hourly Embedded Machine Learning jobs in Saint Louis, MO?

For Hourly Embedded Machine Learning jobs in Saint Louis, MO, the most frequently searched job titles are:

What job categories do people searching Hourly Embedded Machine Learning jobs in Saint Louis, MO look for?

The top searched job categories for Hourly Embedded Machine Learning jobs in Saint Louis, MO are:

What cities near Saint Louis, MO are hiring for Hourly Embedded Machine Learning jobs?

Cities near Saint Louis, MO with the most Hourly Embedded Machine Learning job openings:

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in Saint Louis, MO as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $149,124 per year, or $71.7 per hour.

Deep Learning Scientist, Speech Synthesis

Catapult Solutions Group

Saint Louis, MO • On-site, Remote

Contractor

Re-posted 24 days ago

Job description

08/06/26
The hiring manager is looking for people with experience working with autoregressive models, diffusion and flow matching models, FSQ and RVQ codec models, and full-duplex speech-to-speech models.
Deep Learning Scientist - Speech Synthesis
Update on 07/22/26:
No PHD is need just focus on seeking candidates with deep, hands-on expertise in Text-to-Speech (TTS), including experience developing and working with modern SOTA architectures and models. It is essential that their TTS background reflects work with the latest technologies and innovations in speech synthesis, demonstrating current industry knowledge and practical implementation experience.
Update as of 07/21/26: Hi,
We are specifically seeking candidates with deep, hands-on expertise in Text-to-Speech (TTS), including experience developing and working with modern SOTA architectures and models. It is essential that their TTS background reflects work with the latest technologies and innovations in speech synthesis, demonstrating current industry knowledge and practical implementation experience.
Thank you!
Location: 100% Remote (Anywhere in the U.S.)
Duration: 6-Month Contract
Position Overview
We are seeking a Deep Learning Scientist - Speech Synthesis to support the development of next-generation speech AI technologies. This role focuses on training and optimizing speech models, improving model performance, and solving complex machine learning challenges related to speech applications.
The ideal candidate has strong experience in speech synthesis (Text-to-Speech) or Speech-to-Text, deep learning, and Python development. Success in this role requires the ability to analyze model behavior, diagnose training issues, and improve model performance-not just collect or evaluate data.
Key Responsibilities
  • Train and optimize speech synthesis models, including mel spectrogram and vocoder models.
  • Analyze training metrics, validation losses, and model performance to identify root causes of model issues and recommend improvements.
  • Benchmark and optimize speech models across multiple use cases.
  • Improve speech data preparation, augmentation, filtering, and dataset quality.
  • Develop and refine high-quality training datasets for speech AI models.
  • Measure and characterize model accuracy, quality, and bias.
  • Collaborate with cross-functional teams to develop and deliver new speech AI features.
  • Participate in software development, design reviews, testing, and code reviews.
  • Troubleshoot technical issues and contribute to continuous model improvements.
Required Qualifications
  • Master's degree or Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Mathematics, Linguistics, Computational Linguistics, or a related field (or equivalent experience).
  • 3+ years of relevant industry experience.
  • Strong Python programming skills.
  • Strong understanding of machine learning and deep learning concepts.
  • Experience with Text-to-Speech (TTS), Speech Synthesis, or Speech-to-Text (STT) technologies.
  • Hands-on experience training deep learning models using PyTorch.
  • Ability to analyze training behavior, validation losses, and model performance to troubleshoot and improve machine learning models.
  • Knowledge of speech signal processing concepts, including FFT, MFCC, and mel spectrograms.
  • Strong understanding of software development fundamentals.
  • Experience using version control systems such as Git, Gerrit, or GitLab.
  • Excellent communication and collaboration skills.
Preferred Qualifications
  • Experience with deep learning architectures such as CNNs, RNNs, LSTMs, and Transformers.
  • Experience with voice cloning or multilingual speech systems.
  • Knowledge of text normalization (TN), inverse text normalization (ITN), or grapheme-to-phoneme (G2P) systems.
  • Fluency in one or more languages such as Spanish, Mandarin, German, Japanese, Russian, French, Arabic, Hindi, Korean, Italian, or Portuguese.
  • Interest in linguistics, phonetics, and speech technologies.
  • Strong C++ programming skills.
  • Familiarity with GPU technologies such as CUDA, cuDNN, or TensorRT.
  • Experience deploying machine learning models to cloud, data center, or embedded environments.
What We're Looking For
The ideal candidate is someone who enjoys solving difficult machine learning problems and has hands-on experience training speech models. Beyond building models, we're looking for someone who can investigate why a model is underperforming, analyze validation losses, identify root causes, and improve overall model quality and performance.
Additional Information
  • 100% remote position within the United States.
  • No specific U.S. time zone requirement.
  • This is a contract opportunity.
  • Opportunity to contribute to cutting-edge speech AI and deep learning technologies.

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