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Machine Learning Manager information

See Novi, MI salary details

$47.8K

$76.7K

$110.7K

How much do machine learning manager jobs pay per year?

As of Jul 13, 2026, the average yearly pay for machine learning manager in Novi, MI is $76,658.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,900.00 and $86,800.00 per year, depending on experience, location, and employer.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and their role involves understanding algorithms, data processing, and model deployment. While AI automation tools can handle certain tasks, MLEs are essential for creating, optimizing, and overseeing complex AI solutions, making complete replacement unlikely in the near term.

What are some of the main challenges a Machine Learning Manager faces when leading a team?

A Machine Learning Manager often navigates challenges such as balancing project deadlines with the need for thorough experimentation and research, ensuring clear communication between technical and non-technical stakeholders, and fostering collaboration among data scientists, engineers, and product teams. Additionally, managers must keep their team's skills current with rapidly evolving technologies while also addressing issues like data quality and model deployment in production environments. Successfully overcoming these challenges requires strong leadership, adaptability, and a deep understanding of both business objectives and technical intricacies.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning manager or director, often involving leadership, advanced technical skills, and strategic responsibilities. These roles usually require extensive experience, expertise in AI tools and frameworks, and may include performance-based bonuses or stock options that contribute to the total compensation. Such salaries are common in large tech companies or organizations with significant AI investments.

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

To thrive as a Machine Learning Manager, you need a robust background in machine learning algorithms, statistical analysis, and software engineering, typically supported by an advanced degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and project management platforms, along with experience in deploying ML systems, is essential. Strong leadership, communication, and strategic thinking skills set exceptional managers apart, enabling them to guide teams and align projects with business objectives. These skills are crucial to successfully leading technical teams, ensuring project delivery, and translating complex ML solutions into organizational value.

Is ML a high paying job?

Machine Learning Managers typically earn high salaries due to the specialized skills required, such as expertise in algorithms, programming, and data analysis. Compensation varies based on experience, location, and industry, but it is generally above average compared to many other tech roles.

Which 3 jobs will survive AI?

Machine Learning Managers will continue to be essential as they oversee AI projects, interpret complex data, and coordinate teams, tasks that require strategic thinking and human judgment. Roles that involve creative problem-solving, emotional intelligence, and domain-specific expertise, such as healthcare professionals, educators, and skilled tradespeople, are also likely to persist despite AI advancements. These jobs rely on human intuition and adaptability that AI cannot fully replicate.

What are Machine Learning Managers?

Machine Learning Managers are professionals responsible for leading teams that develop, implement, and maintain machine learning models and systems. They oversee data scientists, engineers, and other specialists, ensuring projects align with business goals and are delivered on time. Their role often involves coordinating cross-functional teams, managing project timelines, and staying current with the latest advancements in artificial intelligence and machine learning. Additionally, they may be involved in hiring, mentoring, and providing technical guidance to their team.
What are the most commonly searched types of Machine Learning jobs in Novi, MI? The most popular types of Machine Learning jobs in Novi, MI are:
What are popular job titles related to Machine Learning Manager jobs in Novi, MI? For Machine Learning Manager jobs in Novi, MI, the most frequently searched job titles are:
What cities near Novi, MI are hiring for Machine Learning Manager jobs? Cities near Novi, MI with the most Machine Learning Manager job openings:
Machine Learning Manager jobs near you
Infographic showing various Machine Learning Manager job openings in Novi, MI as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $76,658 per year, or $36.9 per hour.
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

What Stellantis employees say

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