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Deep Learning Engineer Jobs in Michigan (NOW HIRING)

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director ... Expertise in Python with extensive experience in at least one deep learning framework (PyTorch or ...

Showing results 21-40

Deep Learning Engineer information

See Michigan salary details

$33.1K

$101K

$166.9K

How much do deep learning engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for deep learning engineer in Michigan is $100,987.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,300.00 and $132,000.00 per year, depending on experience, location, and employer.

What is a deep learning engineer?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What does a deep learning engineer do?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.

What skills and qualifications does a deep learning engineer need?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

Are deep learning engineers in demand?

Deep learning engineers are in high demand due to the growth of artificial intelligence and machine learning applications across industries such as technology, healthcare, and finance. They typically require skills in neural networks, programming languages like Python, and frameworks such as TensorFlow or PyTorch, with job opportunities increasing as AI adoption expands.

What cities in Michigan are hiring for Deep Learning Engineer jobs?

Cities in Michigan with the most Deep Learning Engineer job openings:

Infographic showing various Deep Learning Engineer job openings in Michigan as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $100,987 per year, or $48.6 per hour.

Staff Machine Learning Engineer - BEV/Multi-Modal Perception

Ann Arbor, MI • On-site

$150 - $200/hr

Other

Posted 2 days ago

New


Job description

  • Lead BEV model development and execute the technical roadmap for BEV-based perception models across detection, segmentation, road topology, and scene understanding
  • Design multi-modal architectures that fuse camera, LiDAR, radar, and HD maps into unified spatial representations
  • Develop foundational perception models using BEV transformers, voxel-based encoders, or implicit scene representations
  • Own large-scale training workflows, including data sampling, augmentation, distributed training, and hyperparameter optimization
  • Improve model robustness and generalization for low visibility, occlusions, and rare scene configurations
  • Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance
  • Collaborate with sensor calibration, mapping, and fusion teams on cohesive perception model interfaces
  • Mentor and guide ML engineers while cultivating experimentation, code quality, and model validation best practices
  • Explore self-supervised learning, large-scale pretraining, and foundation models for 3D perception
Requirements
  • 10+ years of experience in deep learning for perception, 3D vision, and/or autonomous systems
  • M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience)
  • Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion
  • Strong background in multi-modal sensor fusion, particularly integrating camera and LiDAR data
  • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
  • Experience with large-scale data pipelines, distributed training, and experiment management systems
  • Demonstrated leadership in driving ML model innovation and mentoring technical teams
  • Experience with autonomous driving or robotics perception in production environments
  • Experience with MLOps and infrastructure tools (Ray)
  • Hands-on expertise in BEV-based ML architectures, LiDAR-vision fusion, or spatial-temporal modeling
  • Familiarity with 3D labeling, calibration, and sensor simulation pipelines
  • Track record of publications or open-source contributions in top-tier venues (CVPR, ICCV, NeurIPS, ICRA, CoRL)
  • Understanding of performance tradeoffs and deployment constraints (latency, memory, accuracy)
Core Competencies

Expertise in BEV model development and multi-modal sensor fusion, with a strong focus on deep learning for perception and 3D vision. Proven ability to lead technical teams, mentor engineers, and drive innovation in autonomous systems.

Highest-signal resume keywords
  • BEV Modeling
  • 3D Scene Understanding
  • Multi-Modal Sensor Fusion
  • Deep Learning Frameworks (PyTorch, TensorFlow)
Hard Skills
  • Deep Learning for Perception
  • 3D Vision
  • Large-Scale Data Pipelines
  • Distributed Training
  • Hyperparameter Optimization
  • Model Robustness Improvement
  • Self-Supervised Learning
  • Spatial-Temporal Modeling
  • Camera and LiDAR Integration
  • 3D Labeling and Calibration
Soft Skills
  • Mentoring
  • Collaboration
  • Experimentation
  • Code Quality
  • Model Validation Best Practices
Certifications & Qualifications
  • M.S. or Ph.D. in Computer Science
  • Electrical Engineering
  • Robotics
Industry Keywords
  • Autonomous Systems
  • Perception Models
  • Sensor Calibration
  • Mapping and Fusion
  • Publications in CVPR, ICCV, NeurIPS, ICRA, CoRL
Tools & Technologies
  • Python
  • PyTorch
  • TensorFlow
  • Ray
  • Experiment Management Systems
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