Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Forward Deployed Engineer
Ann Arbor, MI · On-site
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Forward Deployed Engineer
Ann Arbor, MI · On-site
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Lead Architect- Automotive AI Cockpit
Plymouth, MI · On-site
$52.50 - $72/hr
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Quick apply
Apply Early
Lead Architect- Automotive AI Cockpit
Plymouth, MI · On-site
$52.50 - $72/hr
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Apply Early
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Lead Architect- Automotive AI Cockpit
Plymouth, MI · On-site
$52.50 - $72/hr
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Lead Architect- Automotive AI Cockpit
Plymouth, MI · On-site
$52.50 - $72/hr
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Strong proficiency in Python and experience with ML frameworks and model fine-tuning techniques * Hands-on experience with inference optimization and model serving infrastructure, including on-prem ...
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Quick apply
Apply Early
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle ... Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC ...
Apply Early
... inference system monitoring, cloud security architecture, and cloud security operations. The role ... Develop and oversee comprehensive security strategies for AI/ML systems throughout their lifecycle.
... inference system monitoring, cloud security architecture, and cloud security operations. The role ... Develop and oversee comprehensive security strategies for AI/ML systems throughout their lifecycle.
... inference system monitoring, cloud security architecture, and cloud security operations. The role ... Develop and oversee comprehensive security strategies for AI/ML systems throughout their lifecycle.
... inference system monitoring, cloud security architecture, and cloud security operations. The role ... Develop and oversee comprehensive security strategies for AI/ML systems throughout their lifecycle.
Systems Engineer - End-to-End Software Diagnostics & Observability
Dearborn, MI · On-site
$159K - $188K/yr
If you are passionate about AI/ML, complex systems, embedded software, and solving real-world ... or inference. * Technical Documentation: 1+ years of experience translating ambiguity into ...
Systems Engineer - End-to-End Software Diagnostics & Observability
Dearborn, MI · On-site
$159K - $188K/yr
If you are passionate about AI/ML, complex systems, embedded software, and solving real-world ... or inference. * Technical Documentation: 1+ years of experience translating ambiguity into ...
... inference system monitoring, cloud security architecture, and cloud security operations. The role ... Develop and oversee comprehensive security strategies for AI/ML systems throughout their lifecycle.
... inference system monitoring, cloud security architecture, and cloud security operations. The role ... Develop and oversee comprehensive security strategies for AI/ML systems throughout their lifecycle.
Systems Engineer
Dearborn, MI · On-site
If you are passionate about AI/ML, complex systems, embedded software, and solving real-world ... or inference. * Technical Documentation: 1+ years of experience translating ambiguity into ...
Systems Engineer
Dearborn, MI · On-site
If you are passionate about AI/ML, complex systems, embedded software, and solving real-world ... or inference. * Technical Documentation: 1+ years of experience translating ambiguity into ...
Data Engineering Manager
Dearborn, MI · On-site
... inference pipelines are highly available and scalable. * Agentic AI Enablement: Lead the data ... Feature Engineering & ML Ops: Collaborate closely with Data Scientists and AI Engineers to ...
Data Engineering Manager
Dearborn, MI · On-site
... inference pipelines are highly available and scalable. * Agentic AI Enablement: Lead the data ... Feature Engineering & ML Ops: Collaborate closely with Data Scientists and AI Engineers to ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Support Ford's AI and ML engineering capability within the TOP platform, including model fine ... inference quality * Partner with the Ford GCP Cloud Engineers to ensure model artifact storage ...
Support Ford's AI and ML engineering capability within the TOP platform, including model fine ... inference quality * Partner with the Ford GCP Cloud Engineers to ensure model artifact storage ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Ml Inference information
What is a $900000 AI job?
A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.
What is ML inference?
ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.
What is the difference between Ml Inference vs Data Scientist?
| Aspect | ML Inference | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning models, programming skills | Degree in data science, statistics, or related fields |
| Work Environment | Deploying models in production, real-time data processing | Data analysis, model development, research |
| Industry Usage | AI product deployment, software companies | Research institutions, tech firms, consulting |
ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.
What engineer makes $500,000 a year?
Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.
Which 3 jobs will survive AI?
Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.
What are some common challenges faced by ML Inference Engineers when deploying models to production?
ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.
Will MLE be replaced by AI?
Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.
What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?
To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
What are popular job titles related to Ml Inference jobs in Michigan? For Ml Inference jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Michigan look for? The top searched job categories for Ml Inference jobs in Michigan are:
What cities in Michigan are hiring for Ml Inference jobs? Cities in Michigan with the most Ml Inference job openings:
Full-time
Medical, Life, Retirement, PTO
Posted 7 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:Â At Bosch, your job matches your lifestyle.
Celebrate success:Â At Bosch, we celebrate you.
Be yourself:Â At Bosch, we value values.
Shape tomorrow:Â 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.Â
By choice, we are committed to a diverse workforce - EOE/Protected Veteran/Disabled.Â
BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics) InitiativesÂ
FIRST Robotics (For Inspiration and Recognition of Science and Technology)Â
AWIM (A World In Motion)Â
Equal Opportunity Employer, including disability / veteransÂ
*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.Â
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