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Remote Tesla Machine Learning Engineer Jobs in Michigan

Detroit Tigers

Cloud Data Engineer

Detroit, MI · On-site +1

$113K - $136K/yr

... machine learning models. This position will report to the Manager, Baseball Systems Data. Key ... The location may be based in Detroit or fully remote. * Occasional evening, weekend, and holiday ...

General Motors

Senior AI/ML Engineer

Lansing, MI · On-site +1

$106K - $145K/yr

Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA ... Experience with computer vision , machine learning , or data‑centric AI projects -- especially ...

Relativity

Senior Applied Scientist

Detroit, MI · On-site +1

Posting Type Remote/Hybrid Job Overview WHO WE ARE Relativity is a leading legal data intelligence ... We work closely with engineering, product, design, data engineering, machine learning operations ...

Deloitte

AI and Data Science Engineer III

Detroit, MI · On-site +1

$113K - $136K/yr

Deliver governed datasets and feature engineering and serving patterns for machine learning training and real-time inference, including online and offline consistency, caching, latency targets, and ...

DISHER

Vision Engineer

Detroit, MI · On-site +1

Vision Engineer - Remote / Travel DISHER is currently partnering with a world leading automation ... Knowledge on machine learning with AI capabilities. * Self-driven and willingness to work long ...

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Remote Tesla Machine Learning Engineer information

What does a Remote Tesla Machine Learning Engineer do?

A Remote Tesla Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models to improve Tesla's products and services. Working from a remote location, they collaborate with teams to analyze large datasets, build predictive models, and optimize algorithms for applications such as autonomous driving, energy management, and manufacturing. They also ensure that machine learning solutions are scalable and meet Tesla's high standards for performance and safety.

What are some common challenges faced by Remote Tesla Machine Learning Engineers, and how can they be overcome?

Remote Tesla Machine Learning Engineers often face challenges such as collaborating across different time zones, ensuring effective communication with cross-functional teams, and maintaining access to high-performance computing resources. To overcome these, engineers typically use collaborative tools for code sharing and project management, participate in regular virtual meetings, and leverage Tesla's robust cloud infrastructure for experimentation and model training. Proactively seeking feedback and staying aligned with team goals are also key practices for success in this remote, fast-paced environment.

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

To thrive as a Remote Tesla Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning principles, typically demonstrated through a relevant degree or equivalent experience. Proficiency with Python, TensorFlow or PyTorch, cloud platforms, and version control systems is crucial, and certifications in AI/ML can be advantageous. Exceptional problem-solving, communication, and self-motivation are important soft skills for collaborating remotely and tackling complex projects. These skills enable engineers to design, implement, and scale innovative AI solutions that drive Tesla's technology forward.

What is the difference between Remote Tesla Machine Learning Engineer vs Remote Data Scientist?

AspectRemote Tesla Machine Learning EngineerRemote Data Scientist
Required CredentialsDegree in Computer Science, Engineering, or related field; experience with ML frameworksDegree in Statistics, Mathematics, or related field; strong programming skills
Work EnvironmentCollaborates with engineering teams on autonomous systems and vehicle dataAnalyzes large datasets to extract insights for business or product decisions
Employer & Industry UsagePrimarily in automotive, tech, and autonomous vehicle sectorsAcross tech, finance, healthcare, and various industries

While both roles involve data analysis and machine learning, the Remote Tesla Machine Learning Engineer focuses on developing algorithms for autonomous vehicles, whereas the Remote Data Scientist analyzes data to inform business strategies. The roles share similar credentials but differ in application and industry focus.

What are popular job titles related to Remote Tesla Machine Learning Engineer jobs in Michigan? For Remote Tesla Machine Learning Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Remote Tesla Machine Learning Engineer jobs in Michigan look for? The top searched job categories for Remote Tesla Machine Learning Engineer jobs in Michigan are:
What cities in Michigan are hiring for Remote Tesla Machine Learning Engineer jobs? Cities in Michigan with the most Remote Tesla Machine Learning Engineer job openings:
Remote Tesla Machine Learning Engineer jobs near you
Generative AI Automation Engineer - Remote Job

Generative AI Automation Engineer - Remote Job

EnthuZiastic

Lansing, MI • Remote

Other

Posted 18 days ago

Job description

About Us

Our mission is to bring people together and connect them into a community to nurture each other. We aim to share a conducive environment, a joyous space to grow and excel; a world brimming with selfless love and enough kindness. We strive to enrich each of our lives with kaleidoscopic memories we make here - vibrant, lively, of all hues and colors.

Job Description

​

This is a remote position.

We are seeking a highly skilled and innovative Generative AI Automation Engineer to join our team. The ideal candidate will be responsible for designing, developing, and implementing automation solutions powered by Generative AI models. This role requires a combination of expertise in machine learning, natural language processing, software engineering, and automation frameworks to drive efficiency and innovation in business processes.

Key Responsibilities:

Generative AI Model Implementation:

  • Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks.

  • Integrate pre-trained models or build custom models for specific use cases.

Automation Design and Development:

  • Design and implement AI-driven workflows and solutions to automate repetitive tasks and improve process efficiency.

  • Develop APIs, scripts, and tools for seamless integration of AI models into existing systems.

Data Management:

  • Collect, preprocess, and analyze large datasets for training and validating AI models.

  • Ensure data privacy and compliance with regulatory requirements during data handling.

System Integration:

  • Collaborate with software development and IT teams to integrate Generative AI solutions with enterprise systems.

  • Build and maintain pipelines for real-time AI inference and automation.

Monitoring and Optimization:

  • Continuously monitor AI automation solutions to ensure accuracy, efficiency, and reliability.

  • Optimize models and processes based on performance metrics and user feedback.

Research and Innovation:

  • Stay updated with the latest advancements in Generative AI and automation technologies.

  • Identify opportunities for implementing cutting-edge AI solutions to address business challenges.

Documentation and Collaboration:

  • Document technical designs, workflows, and implementation strategies.

  • Collaborate with cross-functional teams, including product managers, data scientists, and software engineers.

Requirements

Required Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

  • Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, or Hugging Face.

  • Proficiency in designing and deploying machine learning models, particularly in Generative AI.

  • Experience with automation tools (e.g., RPA, workflow orchestration tools).

  • Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and containerization technologies (Docker, Kubernetes).

  • Solid understanding of data structures, algorithms, and software design principles.

  • Strong analytical and problem-solving skills.

  • Excellent communication and teamwork abilities.

Preferred Qualifications:

  • Experience with NLP, image generation, or multimodal AI models.

  • Hands-on experience with APIs for AI services like OpenAI, Cohere, or Google AI.

  • Familiarity with prompt engineering and fine-tuning Generative AI models.

  • Knowledge of MLOps practices for deploying and maintaining AI solutions.

  • Previous experience in automation or workflow optimization projects.

Benefits

Why Join Us?

  • Work with cutting-edge Generative AI technologies.

  • Collaborate with a team of forward-thinking innovators.

  • Make a tangible impact on the future of automation and AI-driven processes.

If you are passionate about leveraging Generative AI to create innovative automation solutions, we invite you to apply and be a part of our dynamic and growing team.

Apply