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Deep Learning Quantization Jobs in Ann Arbor, MI

Bosch Group

Technical Lead- AI Cockpit

Plymouth, MI

Strong hands-on expertise in machine learning, deep learning, and AI system design, with experience ... Strong knowledge of edge AI optimization techniques (quantization, pruning, distillation) and ...

Bosch Group

Technical Lead- AI Cockpit

Plymouth, MI

Strong hands-on expertise in machine learning, deep learning, and AI system design, with experience ... Strong knowledge of edge AI optimization techniques (quantization, pruning, distillation) and ...

Bosch Group

Technical Lead- AI Cockpit

Plymouth, MI · On-site

Strong hands-on expertise in machine learning, deep learning, and AI system design, with experience ... Strong knowledge of edge AI optimization techniques (quantization, pruning, distillation) and ...

Voiceflow

Senior ML Engineer - Mapping

Ann Arbor, MI · On-site

$210 - $250/hr

Develop data curation, auto‑labeling, and active learning pipelines. * Develop evaluation scripts ... Deep expertise in several of the following areas: * Computer Vision Foundations: Object detection ...

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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Ann Arbor, MI

Deep Learning Quantization information

See Ann Arbor, MI salary details

$10.8K

$82.1K

$137K

How much do deep learning quantization jobs pay per year?

As of Aug 7, 2026, the average yearly pay for deep learning quantization in Ann Arbor, MI is $82,068.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,400.00 and $136,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a deep learning quantization engineer, and why are they important?

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

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

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.
What are popular job titles related to Deep Learning Quantization jobs in Ann Arbor, MI? For Deep Learning Quantization jobs in Ann Arbor, MI, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Ann Arbor, MI look for? The top searched job categories for Deep Learning Quantization jobs in Ann Arbor, MI are:
What cities near Ann Arbor, MI are hiring for Deep Learning Quantization jobs? Cities near Ann Arbor, MI with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you

Principal Machine Learning (ML)/ Artificial Intelligence (AI) Engineer- AI Cockpit

Bosch Group

Plymouth, MI • On-site

Full-time

Medical, Life, Retirement, PTO

Posted 28 days ago

Job description

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Our areas of activity are every bit as diverse as our outstanding Bosch teams around the world. Their creativity is the key to innovation through connected living, mobility, or industry.

Let's grow together, enjoy more, and inspire each other. Work #LikeABosch

 Reinvent yourself: At Bosch, you will evolve.
Discover new directions: At Bosch, you will find your place.
Balance your life: At Bosch, your job matches your lifestyle.
Celebrate success: At Bosch, we celebrate you.
Be yourself: At Bosch, we value values.
Shape tomorrow: At Bosch, you change lives.

Job Description

Role Overview

We are seeking a highly skilled Technical Lead / Architect to drive the design, development, and delivery of next-generation AI-powered smart cockpit solutions for Automotive. This role will lead the technical vision and execution of an intelligent, context-aware in-vehicle experience that transforms the cockpit into a proactive, personalized companion for drivers and passengers.

You will work at the intersection of AI, embedded systems, and automotive software, shaping scalable architectures while coordinating cross-functional teams to bring innovative cockpit experiences to production.

Key Responsibilities

  • Define and own the end-to-end architecture for AI-driven smart cockpit systems across hardware, middleware, and application layers
  • Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle management
  • Architect multi-model/LLM orchestration and on-device model adaptation (fine-tuning, distillation) for edge automotive deployment, including model selection, routing, and inference optimization on GPU/NPU SoCs
  • Architect efficient edge AI execution, optimizing models for CPU/GPU/NPU (quantization, pruning, latency, power)
  • Design hybrid edge-cloud architectures for personalization, continuous learning, and scalable feature delivery
  • Integrate multimodal AI capabilities (voice, vision, sensor fusion) with real-time and safety-critical constraints
  • Establish robust data pipelines, telemetry, and feedback loops, enabling continuous model validation, performance monitoring, and iterative improvement of AI capabilities in production
  • Define the AI evaluation and observability framework, including model quality testing, regression checks, and production monitoring tied to release readiness
  • Drive technical program execution across architecture, AI, and integration workstreams - including planning, dependencies, risk management, milestone tracking, cross-functional coordination, and stakeholder communication - to ensure on-time, high-quality production readiness
Qualifications

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field with 5+ years of experience in AI/ML-driven system development.
  • Strong hands-on expertise in machine learning, deep learning, and AI system design, with experience deploying models on edge, embedded, or automotive platforms.
  • Experience integrating AI and Generative AI/LLM-based capabilities into real-time, resource-constrained environments.
  • Strong knowledge of edge AI optimization techniques (quantization, pruning, distillation) and proficiency in Python and C++, along with experience in embedded AI lifecycle management, including OTA updates and fleet telemetry.
  • Hands-on experience with LLM/generative-AI orchestration and model fine-tuning/adaptation in real-time, resource-constrained environments.
  • Strong understanding of cloud platforms architecture and edge-cloud orchestration, and proven ability to lead cross-functional engineering teams.

Preferred Qualifications

  • Knowledge of automotive cockpit systems (voice assistants & personalization)
  • Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC/processors
  • Exposure to user experience design for in-vehicle systems

Key Competencies

  • Strong technical leadership and architectural thinking
  • Ability to balance innovation with production readiness
  • Excellent communication and stakeholder management skills
  • Strategic mindset with hands-on execution capability
  • Passion for shaping the future of AI-defined vehicles

What You'll Work On

  • AI-powered assistants that understand driver intent and context
  • Personalized, self-learning cockpit experiences
  • Seamless integration of infotainment, productivity, and safety features
  • Next-gen human-machine interfaces (voice, gesture, multimodal AI)
Additional Information

All your information will be kept confidential according to EEO guidelines. 

By choice, we are committed to a diverse workforce - EOE/Protected Veteran/Disabled. 

BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics) Initiatives 

FIRST Robotics (For Inspiration and Recognition of Science and Technology) 

AWIM (A World In Motion) 

Equal Opportunity Employer, including disability / veterans 

*Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date. 

Your well-being matters at Bosch! We offer a competitive compensation and a benefits package designed to empower you in every area of your life. This includes premium health coverage, a 401(k) with generous matching, resources for financial planning and goal setting, ample paid time off, parental leave, and comprehensive life and disability protection. We're investing in your success!

#LI- JM1

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