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Senior Embedded Machine Learning Jobs in Novi, MI

Optimal Inc.

Robotics Software Engineer

Warren, MI · On-site

Familiarity with robotics frameworks (ROS 2) and machine learning is a plus. Key Responsibilities * Develop embedded software for signal processing, sensor integration, and data acquisition * Design ...

Optimal Inc.

Robotics Software Engineer

Warren, MI · On-site

Familiarity with robotics frameworks (ROS 2) and machine learning is a plus. Key Responsibilities * Develop embedded software for signal processing, sensor integration, and data acquisition * Design ...

Wayve

Machine Learning Engineer, App SW

Detroit, MI · Hybrid

$283K - $381K/yr

Mentor senior engineers and shape the long-term technical direction across Autonomy. About you: In order to set you up for success as a Machine Learning Engineer at Wayve, we're looking for the ...

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All JobsEmbedded Machine Learning JobsSenior Embedded Machine Learning JobsSenior Embedded Machine Learning Jobs in Novi, MI

Senior Embedded Machine Learning information

See Novi, MI salary details

$70.8K

$135.8K

$181.5K

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

As of Jul 13, 2026, the average yearly pay for senior embedded machine learning in Novi, MI is $135,825.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,300.00 and $152,500.00 per year, depending on experience, location, and employer.

What is the difference between Senior Embedded Machine Learning vs Embedded Software Engineer?

AspectSenior Embedded Machine LearningEmbedded Software Engineer
Required CredentialsBachelor's/Master's in CS, EE, or related; experience in ML and embedded systemsBachelor's in CS, EE, or related; strong programming skills in C/C++
Work EnvironmentDeveloping ML models for embedded devices, hardware integrationDesigning and implementing embedded software for devices
Industry UsageAI/ML-focused companies, IoT, consumer electronicsAutomotive, industrial, consumer electronics

While both roles involve embedded systems, Senior Embedded Machine Learning focuses on integrating ML models into hardware, requiring knowledge of AI and data science. Embedded Software Engineers primarily develop software for embedded devices, emphasizing firmware and system-level programming. The roles overlap in embedded environment skills but differ in their core focus on AI versus traditional software development.

What are some common challenges faced by Senior Embedded Machine Learning Engineers when deploying models on edge devices?

Senior Embedded Machine Learning Engineers often encounter challenges such as optimizing model size and inference speed to fit within the limited computational resources and memory of edge devices. Balancing accuracy and performance while minimizing power consumption is critical, especially for battery-operated products. Additionally, integrating models with existing embedded software and ensuring reliable, real-time operation can require close collaboration with hardware and firmware teams. Staying current with advancements in model compression and hardware acceleration is also essential for success in this role.

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

To thrive as a Senior Embedded Machine Learning Engineer, you need expertise in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often backed by an advanced degree in computer science or electrical engineering. Familiarity with tools such as TensorFlow Lite, ONNX, and embedded hardware platforms (e.g., ARM Cortex-M, NVIDIA Jetson) is typically required. Strong problem-solving, project management, and communication skills distinguish top performers in this role. These capabilities are crucial for efficiently deploying optimized machine learning models on resource-constrained devices and effectively collaborating across multidisciplinary teams.

What does a Senior Embedded Machine Learning engineer do?

A Senior Embedded Machine Learning engineer designs, develops, and optimizes machine learning models to run efficiently on resource-constrained embedded devices such as microcontrollers, IoT devices, and edge hardware. They are responsible for integrating ML algorithms with embedded systems, ensuring low latency and minimal power consumption. Their work often involves collaborating with hardware engineers and software developers to deploy intelligent features in products like smart sensors, wearables, and autonomous systems.
What cities near Novi, MI are hiring for Senior Embedded Machine Learning jobs? Cities near Novi, MI with the most Senior Embedded Machine Learning job openings:
Senior Embedded Machine Learning jobs near you
Sr. Staff Data Scientist - Machine Learning & AI (Quality, Vehicle & Engineering Analytics)

Sr. Staff Data Scientist - Machine Learning & AI (Quality, Vehicle & Engineering Analytics)

Stellantis

Auburn Hills, MI • On-site

Full-time

Posted 26 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 128 frontline employees who took The Breakroom Quiz

15th of 44 rated automakers

Job description

About the Role:
We are looking for a Senior Staff Data Scientist (ML/AI) to serve as a technical leader, architect, and individual contributor within the Machine Learning & AI Engineering team at Stellantis.
This role sits at the intersection of machine learning, advanced analytics, experimentation, and large-scale vehicle/IoT data systems. You will define and influence how ML and AI are used across vehicle quality, engineering systems, and customer experience outcomes.
This is a high-impact, senior IC role (Staff/Principal level influence) responsible for shaping technical strategy, designing scalable ML systems, and driving measurable business outcomes such as quality improvement, warranty reduction, and customer experience enhancement.
What You Will Do:
Technical Leadership & ML Strategy (Staff-Level Ownership)
  • Define and evolve the ML/AI architecture and framework supporting quality, engineering, and vehicle analytics across the organization
  • Set technical direction for:
    • Machine learning systems
    • Experimentation platforms
    • Data science architecture
  • Act as a trusted technical advisor to senior leadership on:
    • Model feasibility
    • Trade-offs (accuracy, scalability, cost, interpretability)
    • Business impact of ML/AI initiatives
  • Influence roadmap decisions across engineering and product organizations

Advanced Machine Learning & Statistical Modeling
  • Develop and deploy predictive, prescriptive, and causal models using:
    • Vehicle data
    • IoT sensor data
    • Enterprise datasets
  • Apply advanced techniques including:
    • Statistical modeling
    • Machine learning algorithms
    • Deep learning / neural networks
  • Lead root cause analysis for vehicle quality, performance, and system failures
  • Design and build LLM-based systems and agentic AI solutions for engineering and quality use cases

Data Science Platform & Scalable Systems
  • Architect and guide development of large-scale distributed data and ML systems
  • Build and scale analytics pipelines using Spark-based distributed processing frameworks
  • Lead ML model lifecycle management, including:
    • Training
    • Validation
    • Deployment
    • Monitoring in production
  • Ensure models and systems are:
    • Explainable
    • Reliable
    • Production-ready
    • Compliant with automotive/regulatory standards

Experimentation & Product Impact
  • Own and evolve the experimentation framework/platform for safe, scalable testing of vehicle and software features
  • Design statistically sound experiments (A/B tests and beyond)
  • Translate experimental results into clear product and engineering decisions
  • Drive measurable business outcomes including:
    • Warranty cost reduction
    • Improved product quality
    • Enhanced customer experience
    • Revenue-impacting insights

Influence, Mentorship & Knowledge Sharing
  • Mentor senior and mid-level data scientists, raising technical standards across the team
  • Help teams with:
    • Problem formulation
    • Research design
    • Statistical interpretation
  • Contribute to internal knowledge systems and external-facing technical content (e.g., blogs or papers)
  • Serve as a cross-functional leader bridging engineering, product, and executive teams

What Success Looks Like (Top Performers)
Strong candidates will demonstrate:
  • Proven impact from deployed ML systems or production analytics products
  • Quantifiable improvements in:
    • Vehicle quality
    • Warranty reduction
    • Customer experience metrics
  • Ability to influence technical strategy beyond their immediate team
  • Strong communication skills with executive and non-technical stakeholders

Demonstrated ability to turn complex analysis into business decisions and outcomes
Basic Qualifications:
  • Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
  • A minimum of 8 years of experience in data science, advanced analytics, or machine learning, including a minimum of 5 years of hands-on experience with Databricks, Palantir, Snowflake, or AWS SageMaker
  • Expert-level proficiency in:
    • Python (or R)
    • SQL
  • Strong foundation in:
    • Machine learning algorithms
    • Statistical modeling
    • Neural networks / deep learning
  • Experience building ML solutions on distributed systems (e.g., Spark)

Preferred Qualifications:
  • Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
  • Experience with:
    • Large Language Models (LLMs)
    • Fine-tuning foundation models
    • Agentic AI systems
  • Experience building ML solutions in engineering, automotive, propulsion, or battery systems
  • Strong understanding of vehicle quality (QA), reliability, or manufacturing analytics
  • Experience working in high-scale enterprise or regulated environments

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