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Research Federated Learning Jobs (NOW HIRING)

webAI Inc

Senior Machine Learning Engineer

Austin, TX · On-site

$103.60K - $142.20K/yr

Productionize AI models from research prototypes into scalable, deployable systems used in real ... Experience with edge AI, federated learning, or offline inference systems. * Understanding of AI ...

Centific

Technical Intern

$40/hr

Join Our PhD Research Internship Program! 🌟 Are you a PhD / MS student working in NLP, Computer ... Federated Learning / Differential Privacy * Red Teaming * ML Ops * LLM Ops / Observability 💡 ...

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$2.4K

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$7.7K

How much do research federated learning jobs pay per month?

As of May 30, 2026, the average monthly pay for research federated learning in the United States is $5,290.17, according to ZipRecruiter salary data. Most workers in this role earn between $3,000.00 and $7,500.00 per month, depending on experience, location, and employer.

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

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 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 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 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.

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What cities are hiring for Research Federated Learning jobs? Cities with the most Research Federated Learning job openings:
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What job categories do people searching Research Federated Learning jobs look for? The top searched job categories for Research Federated Learning jobs are:
Research Federated Learning jobs near you
Infographic showing various Research Federated Learning job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 97% Full Time, and 2% Contract. Highlights an 98% Physical, and 2% Hybrid job distribution, with an average salary of $63,482 per year, or $30.5 per hour.
Senior Staff Machine Learning Engineer - Ads Prediction, Signals & Quality

Senior Staff Machine Learning Engineer - Ads Prediction, Signals & Quality

Apple

Cupertino, CA

$212K - $386.30K/yr

Full-time

Medical, Dental, Retirement

Posted 25 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

At Apple, we focus deeply on our customers’ experience. Apple Ads brings this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses!
Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to big, global brands. Because when advertising is done right, it benefits everyone!
Description
We are seeking an experienced Machine Learning Engineer to drive innovation in ad prediction, quality, and privacy-preserving signals. This role spans three critical areas: building large-scale prediction models, designing signals that respect user privacy, and ensuring ad quality that aligns with Apple’s values of trust and transparency. You will set technical direction, lead complex initiatives, and mentor engineers while collaborating closely with research, infrastructure, and product teams.
This role offers the opportunity to shape the future of privacy-first advertising at Apple. You’ll work with some of the best engineers and researchers in the field, solve problems at massive scale, and deliver models that respect users while driving meaningful outcomes for advertisers.","responsibilities":"10+ years of experience applying ML at scale in ads, recommender systems, content ranking, or related domains.
Strong expertise in deep learning architectures (Transformers, LLMs, DNNs) and training frameworks (TensorFlow, PyTorch).
Proven track record in prediction systems (CTR, CVR, or related) and explore/exploit strategies (bandits, RL).
Experience with privacy-preserving ML (federated learning, differential privacy, homomorphic encryption, secure multiparty computation) is preferred.
Familiarity with large-scale data pipelines, A/B testing infrastructure, and production experimentation.
Strong coding skills in Python and production experience in at least one of: Scala, Java, C++.
Ability to set technical direction, influence cross-functional stakeholders, and deliver business impact.
Bachelor's in Computer Science, Machine Learning, or a related technical field.
Preferred Qualifications
MS or PhD, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field.
Great foundation in information retrieval, including query-document matching, embedding-based ranking, and learning-to-rank algorithms is a plus
Minimum Qualifications
MS or PhD in Computer Science, Machine Learning, or related discipline.
Published research or open-source contributions in ads, ranking, privacy-preserving ML, or large-scale prediction systems.
Experience leading multi-team or cross-org initiatives with measurable business and user impact.
Deep expertise in signals engineering for ads quality, trust & safety, or search relevance.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $212,000 and $386,300, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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