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Embedded Machine Learning Engineer Jobs in Louisiana

$106.40K - $127.80K/yr

Perform exploratory data analysis (EDA) , feature engineering, and data preprocessing on structured and unstructured datasets. * Develop, train, evaluate, and optimize machine learning and deep ...

Embedded Firmware Engineer

Baton Rouge, LA · On-site

$80.80K - $110.70K/yr

This role is ideal for an embedded engineer who enjoys working close to hardware and defining ... device machine learning while owning firmware quality, validation strategy, and long-term ...

Embedded Firmware Engineer

Baton Rouge, LA

$80.80K - $110.70K/yr

This role is ideal for an embedded engineer who enjoys working close to hardware and defining ... device machine learning while owning firmware quality, validation strategy, and long-term ...

Job Requisition ID # 26WD94803 Senior Principal Machine Learning Engineer, ML Platform and Systems Architecture Position Overview The work we do at Autodesk touches nearly every person on the planet.

Job Requisition ID # 26WD97132 26WD97132, Pr incipal Machine Learning Engineer, ML Platform and Systems Architecture French translation to follow!/Traduction francaise a suivre! Position Overview The ...

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Embedded Machine Learning Engineer information

See Louisiana salary details

$59.9K

$131.2K

$148.8K

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

As of May 29, 2026, the average yearly pay for embedded machine learning engineer in Louisiana is $131,162.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,400.00 and $147,900.00 per year, depending on experience, location, and employer.

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

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Louisiana? For Embedded Machine Learning Engineer jobs in Louisiana, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Louisiana look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Louisiana are:
What cities in Louisiana are hiring for Embedded Machine Learning Engineer jobs? Cities in Louisiana with the most Embedded Machine Learning Engineer job openings:
Machine Learning Engineer

$106.40K - $127.80K/yr

Full-time

Posted 9 days ago


Accenture Federal Services rating

8.4

Company rating: 8.4 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

47th of 424 rated business services


Job description

Why Avanade? Because there's literally no place like this

We have two parent companies that give us a strong Microsoft ecosystem with space to be ourselves.People who thrive here are motivated, interested in learning and genuinely have a desire to be the best at what they do. If that sounds like you, then we're the perfect match. You will have the opportunity to utilize the most advanced technology within the Microsoft ecosystem, collaborating with some of the world's largest and most renowned companies, as well as working alongside highly intelligent individuals. This environment allows you to make a significant impact on your career trajectory. If you are looking to enhance your skills and drive transformation within businesses, there is no better place to be.

The EME AI Delivery Hub

AI-and particularly Generative AI-is expected to profoundly impact every company over the coming years. Thanks to Microsoft and Avanade's strategic investments in AI and OpenAI, we are uniquely positioned to help our clients become AI-first organizations.

The EME AI Delivery Hub is an Iberia-based nearshore delivery center serving European and Middle Eastern clients, specialized in end-to-end AI and Advanced Analytics solutions. By joining the Hub, you will be part of a delivery pod working in an agile setup, owning AI initiatives from problem framing and data exploration to model development, deployment, and adoption. You will work closely with clients, guiding them throughout their AI and GenAI transformation journey.

Job Overview

As a Senior Analyst - AI & Data Science, you will design, develop, and deliver AI- and data-driven solutions that help our clients achieve measurable business outcomes. This role combines strong Data Science foundations with hands-on AI engineering, including recent GenAI use cases.

You will work across the full data science lifecycle: data exploration, feature engineering, model development, evaluation, and deployment, while also contributing to modern AI solutions such as LLM-based applications, NLP, computer vision, and predictive analytics, primarily on Microsoft Azure.

Key Role Responsibilities

Day-to-day you will:

  • Design and deliver end-to-end Data Science and AI solutions, from business understanding and data exploration to model deployment and monitoring.
  • Perform exploratory data analysis (EDA), feature engineering, and data preprocessing on structured and unstructured datasets.
  • Develop, train, evaluate, and optimize machine learning and deep learning models, selecting appropriate algorithms and validation strategies.
  • Contribute to Generative AI solutions, including LLM-based applications, prompt engineering, RAG architectures, and applied NLP use cases.
  • Translate business problems into analytical and ML formulations, clearly explaining trade-offs and results to both technical and non-technical stakeholders.
  • Support the preparation of client presentations, demos, and proposals, articulating analytical insights and AI-driven value.
  • Stay up to date with the latest advancements in Data Science, ML, DL, and GenAI, and actively share knowledge within the team.
  • Contribute to reusable assets such as code templates, analytical frameworks, and internal training materials.
  • Collaborate with senior team members and architects to identify opportunities where advanced analytics and AI can transform client operations.

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Key Role Skill & Capability Requirements

Core Skills

  • Strong foundation in Data Science and applied Machine Learning, including supervised and unsupervised learning.
  • Hands-on experience with ML/DL frameworks (e.g., scikit-learn, PyTorch, TensorFlow or equivalent).
  • Solid understanding of model evaluation, validation, and performance metrics.
  • Experience working with structured and unstructured data, including text data for NLP use cases.
  • Proficiency in Python for data analysis and ML development.

AI & GenAI

  • Experience or strong interest in Generative AI, including LLMs, embeddings, prompt engineering, and retrieval-based approaches.
  • Familiarity with NLP, computer vision, forecasting, or optimization use cases is a strong plus.
  • Exposure to Azure AI / Azure Machine Learning / Azure OpenAI is highly valued.

Professional Skills

  • Strong analytical and problem-solving mindset, with the ability to structure ambiguous problems.
  • Ability to communicate insights clearly in English and Spanish, both written and verbal.
  • Comfortable working in agile, client-facing environments.

Preferred Education Background

You likely hold a bachelor's and/or master's degree in computer science, Data Science, Statistics, Mathematics, Physics, Engineering, or a related quantitative field. Equivalent practical experience is also valued.

Preferred Years of Work Experience:

  • 3+ years of applied experience delivering Data Science, Machine Learning, or AI projects in real-world environments.
  • Experience over the last few years may be heavily focused on GenAI, but grounded in solid ML/DL and analytical fundamentals.

What We offer

  • An accelerated and structured training program on Microsoft Azure and AI services.
  • Hands-on exposure to real client projects across computer vision, NLP, forecasting, and GenAI (Azure OpenAI, chatbots, RAG).
  • Continuous learning through certifications, mentoring, and internal communities of practice.

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