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Deep Learning Ai Jobs in Michigan (NOW HIRING)

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

Whiterabbit.ai

... ai, you will: * Play a key role in architecting the algorithms and models that will power our products * Train on a dedicated high-performance compute cluster specialized for deep learning research

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Deep Learning Ai information

What is the difference between Deep Learning Ai vs Machine Learning Engineer?

AspectDeep Learning AiMachine Learning Engineer
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of neural networksDegree in Computer Science, Data Science, or related fields; programming skills in Python, R
Work EnvironmentResearch labs, AI development teams, tech companies focusing on AI modelsSoftware development teams, data analysis projects across various industries
Industry UsagePrimarily in AI research, autonomous systems, NLP, computer visionAcross industries for predictive modeling, data analysis, automation

Deep Learning Ai specialists focus on designing and implementing neural network models for complex AI tasks, often requiring advanced knowledge of deep neural networks. Machine Learning Engineers develop broader machine learning models, including traditional algorithms. While both roles require similar educational backgrounds, Deep Learning Ai roles are more specialized in neural networks and AI research, whereas Machine Learning Engineers work across a wider range of algorithms and applications.

What is the main job of deep learning in AI?

The main job of deep learning in AI is to develop models that can automatically learn complex patterns and representations from large amounts of data, enabling tasks such as image recognition, natural language processing, and speech understanding. Deep learning engineers design, train, and optimize neural networks using tools like TensorFlow or PyTorch to improve AI system performance.

What are some common challenges faced by professionals working in Deep Learning AI, and how can they be addressed?

Professionals in Deep Learning AI often encounter challenges such as managing large datasets, ensuring model accuracy, and addressing issues like overfitting. Collaboration with data engineers and domain experts is crucial to ensure high-quality data and relevant feature selection. Additionally, staying up-to-date with rapidly evolving frameworks and algorithms requires continuous learning and participation in knowledge-sharing within the team. Regular code reviews and experimentation with different architectures can help overcome technical obstacles and improve model performance.

What is the salary of AI and deep learning?

The salary for roles in AI and deep learning varies based on experience, location, and education, but typically ranges from $80,000 to over $150,000 annually for skilled professionals. Entry-level positions may start around $70,000, while senior roles or those requiring advanced skills in machine learning frameworks and programming languages can earn higher salaries.

What are the key skills and qualifications needed to thrive as a Deep Learning AI Engineer, and why are they important?

To thrive as a Deep Learning AI Engineer, you need a strong background in mathematics, programming (especially Python), and experience with neural networks, typically supported by a degree in computer science, engineering, or a related field. Proficiency with deep learning frameworks such as TensorFlow or PyTorch, and knowledge of tools like CUDA for GPU acceleration, are essential; relevant certifications can be advantageous. Analytical thinking, creativity, and effective communication are important soft skills for solving complex problems and collaborating with cross-functional teams. These skills and qualities are crucial for building robust AI models and driving innovation in this rapidly evolving field.

Which 3 jobs will survive AI?

Deep Learning AI professionals will continue to find roles in research, model development, and AI ethics, as these areas require specialized expertise and human oversight. Jobs involving complex problem-solving, creativity, and emotional intelligence, such as AI research scientists, data scientists, and AI ethics specialists, are less likely to be fully automated. Skills in programming, data analysis, and understanding of AI frameworks will remain valuable in these roles.

What are Deep Learning AI professionals?

Deep Learning AI professionals are experts who design, develop, and implement artificial intelligence systems that use deep neural networks to analyze complex data and solve tasks such as image recognition, natural language processing, and autonomous decision-making. They work with large datasets and advanced algorithms to build models that can learn and improve over time. These professionals often have a background in computer science, mathematics, or engineering, and are skilled in programming languages like Python and frameworks such as TensorFlow or PyTorch.

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 AI researcher, machine learning director, or AI executive, often requiring advanced skills, extensive experience, and leadership responsibilities. These roles may involve overseeing AI projects, developing innovative algorithms, and managing teams, with compensation reflecting the expertise and impact of the position.
What are popular job titles related to Deep Learning Ai jobs in Michigan? For Deep Learning Ai jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Deep Learning Ai jobs in Michigan look for? The top searched job categories for Deep Learning Ai jobs in Michigan are:
What cities in Michigan are hiring for Deep Learning Ai jobs? Cities in Michigan with the most Deep Learning Ai job openings:
Deep Learning Ai jobs near you
Infographic showing various Deep Learning Ai job openings in Michigan as of July 2026, with employment types broken down into 73% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 67% Physical, 2% Hybrid, and 31% Remote job distribution.
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 10 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 130 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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