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Research Machine Learning Federated Learning Jobs

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Research, analyze, support, and implement machine learning solutions on the Snowflake Cloud data warehouse platform using the Snowpark framework Develop novel solutions using knowledge of the latest ...

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

We apply deep learning research to large scale neural datasets to decode internal thought directly ... You will design and implement advanced machine learning models for EEG-based neural decoding ...

LLNL

Machine Learning Researcher

Livermore, CA · On-site

Research, develop, implement, and evaluate new machine learning and data analysis techniques for multiple applications in a collaborative scientific environment. * Actively participate with project ...

OpenAI

Research Engineer, Privacy

San Francisco, CA · On-site

$120 - $150/hr

... machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning ... Have hands‑on research or production experience with PETs. * Are fluent in modern deep‑learning ...

AI Chopping Block

$160 - $240/hr

Set the technical bar for research rigor, judgment, and taste across the organization. Requirements * Deep experience building or evolving real machine learning systems used in production. * Strong ...

LLNL

Machine Learning Researcher

Livermore, CA · On-site

$9.8K/wk

Research, develop, implement, and evaluate new machine learning and data analysis techniques for multiple applications in a collaborative scientific environment. * Actively participate with project ...

LLNL

Machine Learning Researcher

Livermore, CA · On-site

Research, develop, implement, and evaluate new machine learning and data analysis techniques for multiple applications in a collaborative scientific environment. * Actively participate with project ...

Bjak

VP of Research, Machine Learning

Charleston, WV · Remote

Set the technical bar for research rigor, judgment, and taste across the organization. Requirements * Deep experience building or evolving real machine learning systems used in production. * Strong ...

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Research Machine Learning Federated Learning information

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$25.5K

$42.6K

$88K

How much do research machine learning federated learning jobs pay per year?

As of Aug 25, 2026, the average yearly pay for research machine learning federated learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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

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 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.

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Research Machine Learning Federated Learning jobs near you
Infographic showing various Research Machine Learning Federated Learning job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 100% In-person job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Research Engineer

New York, NY • On-site

Tower Research Capital
Finance and Insurance • 1 - 5K employees
Learn more about Tower Research Capitalexternal

$224K/yr

Full-time

PTO

Posted 14 days ago

Job description

Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities.
Tower is home to some of the world's best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization.
Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance.
At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do - combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.
At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.
Summary:
As an AI/ML Applied Research Engineer, you will sit at the cutting-edge intersection of our central machine learning infrastructure and our research teams. Your core mandate is to act as "Customer Zero" for our internal ML Research platform.
You will focus on expanding our ML research platform to benchmark, rapidly prototype, and stress-test both software and hardware layers across our entire distributed ML stack. By leveraging AI agents and auto-research capabilities, you will push our systems to their limits, identify bottlenecks, and create a frictionless environment to test novel machine learning models on realistic, large-scale data.
Ultimately, by hands-on testing these systems yourself, you will act as a technical advisor. You will share insights on research progress, evaluate how new ideas fare in practice, and help guide the strategic direction of our central engineering efforts.
Responsibilities:
  • Platform Validation & Infrastructure Benchmarking:
    • Serve as the primary feedback loop for the entire ML stack.
    • Actively run complex models through our full ML pipeline to comprehensively test both the training and inference environments.
    • Validate the central infrastructure in practice, seeing exactly how new research ideas fare and identifying system bottlenecks before broader rollout to research teams.
  • Streamline Rapid Prototyping for ML Research:
    • Build high-level abstractions that allow users to bypass setup friction.
    • Integrate our core ML tooling directly with our underlying simulation and data frameworks, providing a unified entry point to access our full tech stack.
    • Enable rapid iteration on real-world data and seamless distributed training via Ray.
  • Agentic Workflows for ML Research:
    • Leverage AI agents and auto-research workflows to autonomously generate experiments, stress-test our distributed clusters, and provide data-driven, actionable feedback on what infrastructure needs to be optimized or built next.
  • Research Platform Feedback & Insights Sharing:
    • Act as the critical bridge between infrastructure builders and ML researchers.
    • Be the first to exhaustively test new models and push the platform's limits.
    • Document and publish empirical findings on system capabilities and hardware performance.
    • Take your validated insights to assist engineering teams with platform improvements and advise researchers on how to best leverage the stack.

Qualifications:
  • Strong Software Engineering Foundation:
    • Deep proficiency in Python and software design principles.
    • Ability to build clean, scalable APIs and abstractions that other developers and researchers are enthusiastic about using.
  • Applied Machine Learning:
    • Hands-on experience with modern frameworks (PyTorch, TensorFlow, etc.)
    • Strong practical understanding of how to train, evaluate, and deploy models at scale.
  • Distributed Compute:
    • Experience scaling ML workloads across GPUs and multi-node clusters using frameworks like Ray, Dask, or PyTorch Distributed.
  • AI Agent Workflows:
    • Familiarity with LLM tooling, agentic frameworks, and using AI to automate coding, research, or testing tasks.
  • System Profiling & Optimization:
    • Ability to debug and identify bottlenecks across hardware and software layers (e.g., memory limits, GPU utilization, data pipeline latency).

Nice to Have:
  • Previous experience working in quantitative finance or complex algorithmic research environments.
  • Familiarity with large-scale time-series data, simulation engines, or performance benchmarking.
  • A proven track record of bridging the gap between systems engineering and applied machine learning research.

Anticipated annual base salary range $200,000-$300,000, plus eligible for discretionary bonus.
Tower's headquarters are in the historic Equitable Building, right in the heart of NYC's Financial District and our impact is global, with over a dozen offices around the world.
At Tower, we believe work should be both challenging and enjoyable. That is why we foster a culture where smart, driven people thrive - without the egos. Our open concept workplace, casual dress code, and well-stocked kitchens reflect the value we place on a friendly, collaborative environment where everyone is respected, and great ideas win.
Our benefits include:
  • Generous paid time off policies
  • Savings plans and other financial wellness tools available in each region
  • Hybrid working opportunities
  • Free breakfast, lunch, and snacks daily
  • In-office wellness experiences and reimbursement for select wellness expenses (e.g., gym, personal training and more)
  • Company-sponsored sports teams and fitness events (JPM Corporate Challenge, Cycle for Survival, Wall Street Rides FAR and more)
  • Volunteer opportunities and charitable giving
  • Social events, happy hours, treats, and celebrations throughout the year
  • Workshops and continuous learning opportunities

At Tower, you'll find a collaborative and welcoming culture, a diverse team and a workplace that values both performance and enjoyment. No unnecessary hierarchy. No ego. Just great people doing great work - together.
Tower Research Capital is an equal opportunity employer.

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