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Research Machine Learning Federated Learning Jobs in Michigan

The Machine Learning Engineer will be an essential member of the Research and Development Team, where we engineer large tailor-made systems to solve complex data-related problems from many domains.

... Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy ...

Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy ...

New

Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy ...

New

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $160K/yr

Desired Qualifications * 0-4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with demonstrated ...

Machine Learning Engineer

Detroit, MI · On-site +1

$107K - $241K/yr

As a Machine Learning Engineer, you will design and develop the platforms and frameworks that facilitate automated data-driven decision-making, gather data, and determine statistical algorithms and ...

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer to design, develop, and deploy intelligent solutions leveraging Machine Learning, Large Language ...

... Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy ...

Senior Machine Learning Test Engineer

Novi, MI · On-site +1

$103K - $134K/yr

United States East Coast Position Overview As a Senior Machine Learning Test Engineer in the Research Enablement team, you will work side-by-side with researchers, Machine Learning developers and ...

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Machine Learning Tutor

Detroit, MI · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

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Research Machine Learning Federated Learning information

What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a Researcher in Machine Learning Federated Learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Research Machine Learning Federated Learning jobs in Michigan? For Research Machine Learning Federated Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in Michigan look for? The top searched job categories for Research Machine Learning Federated Learning jobs in Michigan are:
What cities in Michigan are hiring for Research Machine Learning Federated Learning jobs? Cities in Michigan with the most Research Machine Learning Federated Learning job openings:
Machine Learning Engineer

Machine Learning Engineer

Eccalon LLC

Detroit, MI

Full-time

Posted 15 days ago


Job description

Machine Learning Engineer

Location: Detroit, MI- Onsite

Type: Full-time

Security Clearance: No clearance required, must be clearable.

Job Description

The Machine Learning Engineer will be an essential member of the Research and Development Team, where we engineer large tailor-made systems to solve complex data-related problems from many domains. At Eccalon, the projects we support often require solutions that utilize the latest and the best from Deep Learning/Machine Learning research. We support advanced projects in both data constrained and data rich settings. Qualified candidates should be driven and be able to help craft these systems as a part of our R&D team.

Responsibilities

  • Candidates are expected to be familiar with the motions of a classical Machine Learning workflow, and support the team with some of the following tasks:
    • Dataset Creation.
    • Data Exploration/Visualization.
    • Literature Review.
    • Data Wrangling.
    • Implementation and Training of Appropriate Models from Literature.
    • Characterization of Error in Models.
    • Iterative Optimization of Models.
  • On the engineering side of development, the Machine Learning Engineer will have the ability to be hands-on by:
    • Creating training and preprocessing pipelines for faster experimentation.
    • Creating algorithmic modules to interface your Models output with business requirements.
    • Integrating their code to a larger codebase.
    • Putting your model into production using AWS or GCP.

Required Qualifications

  • BS. in Computer Science, or related field.
  • 3+ years of professional Software Development experience in Python.
  • Mastery of Deep Learning fundamentals and statistics underlying Machine Learning.
  • History of software projects putting Machine Learning systems into production in any capacity.
  • History of software projects in general.
  • Deep personal interest with the complete state of the art in a subfield of Machine Learning Research.
  • Ability to work independently, and within a team.
  • Ability to communicate effectively with non-technical stakeholders and supervisors.
  • Prior project experience combining two or more of the following in a production setting:
    • Unsupervised or Semi-supervised Learning.
    • Convolutional Architectures.
    • Autoencoders.
    • Recurrent Architectures for Time-Series Applications.
    • Transformer Architectures for Natural Language Processing.
    • Generative Adversarial Architectures.

Preferred qualifications

  • MS. or PhD in Machine Learning, or related field
  • Extensive AWS or GCP experience putting scalable Machine Learning systems into production.
  • Experience working with extremely high volume / high throughput data in a data lake / data warehousing / training / production environment.
  • Has implemented cutting edge methods (e.g. a custom layer) from recent Machine Learning publications / conference proceedings and has done so in PyTorch or Tensorflow.
  • Publications in AI/ML journals or conferences.

Equal Employment Opportunity (EEO) Policy

Eccalon provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.


Eccalon logo

About Eccalon

Sourced by ZipRecruiter

We are a cross-functional collective of innovative minds that leverages technology to tackle the most challenging problems of this generation for clients, the nation, and the world. Eccalon fosters creativity, curiosity, and imagination across all departments and divisions to pioneer new ideas, products, and services. We advance innovation.​

Industry

Guided missile and space vehicle manufacturing

Company size

11 - 50 Employees

Headquarters location

Hanover, MD, US

Year founded

2017