ZipRecruiter

Log In

1

Hourly Embedded Machine Learning Jobs in Michigan

General Motors

AI/ML Scientist

Milford, MI · On-site

As an Artificial Intelligence and Machine Learning Scientist,you'llbe part of a team that is ... Cross-domain fluency: You connect simulation, embedded systems, and data science to deliver ...

General Motors

AI/ML Scientist

Milford, MI · On-site

As an Artificial Intelligence and Machine Learning Scientist, you'll be part of a team that is ... Cross-domain fluency: You connect simulation, embedded systems, and data science to deliver ...

Whisker

Director of Software Engineering

Auburn Hills, MI

$239K/yr

... embedded systems, mobile apps, cloud infrastructure, Machine Learning engineering capabilities, product ideation and management, and the growth and management of our evolving team to ensure we're ...

Whisker

Director of Software Engineering

Auburn Hills, MI · On-site

$239K/yr

... embedded systems, mobile apps, cloud infrastructure, Machine Learning engineering capabilities, product ideation and management, and the growth and management of our evolving team to ensure we're ...

next page

Showing results 1-20

All JobsHourly Embedded Machine Learning JobsHourly Embedded Machine Learning Jobs in Michigan

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 Michigan? The most popular types of Embedded Machine Learning jobs in Michigan are:
What are popular job titles related to Hourly Embedded Machine Learning jobs in Michigan? For Hourly Embedded Machine Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Hourly Embedded Machine Learning jobs in Michigan look for? The top searched job categories for Hourly Embedded Machine Learning jobs in Michigan are:
What cities in Michigan are hiring for Hourly Embedded Machine Learning jobs? Cities in Michigan with the most Hourly Embedded Machine Learning job openings:
Hourly Embedded Machine Learning jobs near you
Data Scientist - Supply Chain

Data Scientist - Supply Chain

Stellantis

Auburn Hills, MI • On-site

Full-time

Posted 27 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 127 frontline employees who took The Breakroom Quiz

15th of 44 rated automakers

Job description

We're building an AI-enabled supply chain that predicts, prescribes, and acts. As a Supply Chain Data Scientist, you'll develop predictive, prescriptive, optimization, anomaly detection, and simulation models that improve cost, service, and resilience across planning, logistics, and operations.
You will partner with data engineering, AI engineering, and business stakeholders to translate problems into deployable solutions-delivering decision signals that are embedded into operational workflows, planning systems, and agentic experiences.
Responsibilities include but not limited to:
  • Build predictive models for key supply chain processes using statistical, machine learning, and deep learning techniques
  • Develop prescriptive analytics and optimization models to recommend optimal actions under real-world constraints
  • Quantify tradeoffs between cost, service, capacity, and risk
  • Detect variability, disruptions, and anomalies across supply chain operations
  • Build simulations and scenario models to support strategic and operational decisions
  • Partner with stakeholders to translate business problems into data science solutions
  • Enable AI and agentic workflows by producing high-quality predictive and prescriptive signals
  • Merge and analyze large, complex datasets to discover trends, patterns, and actionable insights

Basic Qualifications:
  • Master's degree in data science, statistics, computer science or related field
  • 8+ years of professional experience, including 2+ years in supply chain analytics (planning, forecasting, logistics, manufacturing, or operations)
  • Strong proficiency in Python and SQL
  • Proven experience with predictive modeling (statistical, ML, deep learning), optimization techniques (LP, MIP, constraint programming), and simulation techniques (Monte Carlo, discrete event)
  • Knowledge of advanced statistical techniques and concepts (regression, distributions, statistical tests)
  • Familiarity with a variety of machine learning techniques (clustering, decision trees, neural networks) and their real-world advantages and limitations
  • Experience with data manipulation libraries such as pandas and NumPy
  • Experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
  • Knowledge of big data frameworks and platforms such as Spark, Databricks, or Snowflake

Preferred Qualifications:
  • PhD
  • Experience with supply chain planning, forecasting, or logistics datasets
  • Experience with Databricks, Spark, Snowflake or Palantir Foundry & AIP
  • Experience with MLOps practices (model monitoring, CI/CD for ML)

What Stellantis employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply