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Research Federated Learning Jobs in Michigan (NOW HIRING)

Research Federated Learning information

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

AspectResearch Federated LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm developmentBusiness environments, analytics teams; focus on data analysis and insights
Industry UsageAI research, privacy-preserving ML, distributed systemsBusiness intelligence, marketing, finance, healthcare

Research Federated Learning involves developing privacy-focused, distributed machine learning algorithms, often in research or specialized tech settings. Data Scientists analyze data to generate insights and support decision-making in various industries. While both roles require strong analytical skills, Research Federated Learning emphasizes algorithm development and privacy, whereas Data Scientists focus on data analysis and reporting.

What are the key skills and qualifications needed to thrive as a researcher in federated learning?

To thrive as a Researcher in Federated Learning, you need a strong background in machine learning, distributed systems, and statistics, typically supported by an advanced degree in computer science or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow Federated, and experience with privacy-preserving algorithms are essential. Critical thinking, collaboration, and effective communication are key soft skills for designing experiments and sharing findings with peers. These competencies are vital for advancing privacy-aware AI solutions and producing impactful research in this rapidly evolving domain.

What is a researcher in federated learning?

A Researcher in Federated Learning is a professional who studies, develops, and improves federated learning algorithms and systems. Federated learning is a machine learning approach where data remains decentralized, allowing multiple devices or organizations to collaboratively train models without sharing raw data. These researchers focus on advancing privacy, efficiency, and performance in distributed AI systems. Their work often involves experimenting with new methods, publishing findings, and contributing to the growing field of privacy-preserving machine learning.

What are some common challenges faced by professionals working in research federated learning, and how can they be addressed?

Professionals in Research Federated Learning often encounter challenges such as ensuring data privacy across distributed devices, managing non-iid (non-independent and identically distributed) data, and optimizing communication efficiency between clients and servers. Addressing these issues requires strong collaboration with cross-functional teams, including data engineers, security experts, and software developers, to develop robust protocols and algorithms. Staying updated with the latest research and participating in open-source collaborations can also help overcome technical hurdles and drive innovation in this rapidly evolving field.
What are popular job titles related to Research Federated Learning jobs in Michigan? For Research Federated Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Research Federated Learning jobs in Michigan look for? The top searched job categories for Research Federated Learning jobs in Michigan are:
What cities in Michigan are hiring for Research Federated Learning jobs? Cities in Michigan with the most Research Federated Learning job openings:

Manager, Applied AI, Advanced Informatics

Regeneron Pharmaceuticals

Warren, MI • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 18 hours ago


Regeneron rating

8.7

Company rating: 8.7 out of 10

Based on 42 frontline employees who took The Breakroom Quiz

14th of 86 rated pharmaceutical


Job description

Build our future together:


At Regeneron, we use science and innovation to develop life-changing medicines for people with serious diseases. We are seeking a Manager, Applied AI to join our Advanced Informatics team, supporting health data systems and analytical pipelines across the organization. In this role, you will design and validate applied AI/ML solutions against complex health data and clinical ground truth, while collaborating with senior applied AI leaders, clinical informaticists, data engineers, and production ML engineers to build reusable analytical capabilities the broader informatics team depends on.
This position offers the opportunity to contribute to a fast-growing, science-driven organization making a meaningful difference to patients worldwide.

When & where:
Location: Tarrytown, NY, Armonk, NY, or Warren, NJ

Discover your role:
Frame clinical and business informatics challenges into well-scoped AI/ML problem statements with clear success criteria
Design and build ML pipelines - data ingestion, feature engineering, training, and evaluation - alongside production engineering
Run experiments, benchmarks, and ablation studies to validate model performance and guide modeling decisions
Partner across clinical informatics, data engineering, and ML engineering to bring models into real informatics workflows
Track advances in foundation models, LLMs, and retrieval-augmented generation, and apply them to biomedical and health data
Document research findings and contribute to internal reports, publications, or conference presentations
Explain model behavior, limitations, and performance in terms both technical and non-technical team members can act on

This role requires:
Bachelor's degree in Computer Science, Machine Learning, Data Science, Biomedical Informatics, Statistics, or related field; Master's or Ph.D. strongly preferred with 4-6 years + of progressive experience in applied AI/ML, with demonstrated ability to independently develop and evaluate models (Ph.D. graduates with relevant research experience may be considered)
Solid grounding in supervised, unsupervised, and self-supervised learning, deep neural networks, and modern ML frameworks (PyTorch, TensorFlow, or equivalent)
Strong Python skills across the scientific ML stack (scikit-learn, HuggingFace Transformers, pandas, NumPy)
Experience designing and evaluating NLP or multimodal models, including LLM fine-tuning or prompt engineering
Comfort with experiment tracking, model versioning, and reproducible research practices (MLflow, W&B, DVC, or similar)
Familiarity with cloud-based ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML, or equivalent).
Experience with health or life sciences data - EHR/EMR, claims, clinical notes, genomic, or imaging data
Familiarity with medical terminologies and ontologies (SNOMED CT, ICD-10/11, LOINC, RxNorm, OMOP CDM)
Published work or open-source contributions in applied ML, NLP, or computational biomedicine
Experience with MLOps, CI/CD for ML, or model observability in production
Familiarity with federated learning, privacy-preserving ML, or regulated-environment data use agreements

#AAI

Does this sound like you? Apply now to take your first step towards living the Regeneron Way! We are committed to building a workplace with an inclusive culture. Regeneron is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion or belief (or lack thereof), sex, sexual orientation, gender identity or expression, gender reassignment, marital or civil partnership status, civil status, pregnancy or parental status, age, disability, nationality, citizenship status, ethnic or national origin, membership of the Traveler community, familial status, genetic information, military or veteran status, or any other characteristic protected under applicable law. Where required, we will provide reasonable accommodation to applicants with known disabilities or chronic illnesses during the recruitment process, unless such accommodation would impose undue hardship.

Where necessary, we disclose salary ranges for roles in all countries in which we operate. The final offer will be determined within the relevant range based on the country of employment, specific role level, and your skills and experience. In some countries, collective bargaining agreements (CBAs) may apply and influence certain elements of pay or benefits. Regeneron offers a competitive and comprehensive total rewards package which may include, depending on country and role: annual bonuses or other incentive plans, equity awards, pension or retirement benefits, 401(k) company match, health and wellness programs, fitness centers, insurance benefits (e.g. medical, dental, vision, life and disability), paid time off, and family support benefits. For additional information about Regeneron benefits in the U.S., please visit https://careers.regeneron.com/en/working-at-regeneron/total-rewards/. For other locations, additional information will be provided during the recruitment process. If you have any questions, please speak with your recruiter.


Please be advised that at Regeneron, we believe we do our best work when we are together. For that reason, many roles are required to be performed onsite. Please speak with your recruiter and hiring manager for more information about onsite expectations for your role and location.


As part of the recruitment process, certain background checks may be conducted in accordance with the laws of the country where the position is based. The purpose of such checks is to verify certain information prior to the commencement of employment such as identity, right to work and educational qualifications.


For jobs in Canada: this posting is for an existing position.


Salary Range (annually)

$150,500.00 - $245,500.00

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