Federated Learning jobs

Data Scientist I - Windreich Department for AI & Human Health Research

Mount Sinai Health System · Posted Nov 5, 2026

Open

New York, New York, United States +2 more
Entry
$109K–$164K per year
2+ years

Data Scientist I - Windreich Department for AI & Human Health Research

Mount Sinai Health System · Posted Nov 5, 2026

Open

New York, New York, United States +2 more
Entry
$109K–$164K per year
2+ years

Accelerated Science Intern, Multi-Agent Learning and Coordination

Toyota Research Institute · Posted Nov 5, 2026

Open

Los Altos, California, United States
Hybrid
Internship · Intern
$45–$65 per hour
0+ years

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A paid 12‑week internship focused on developing algorithms and computational systems for multi‑agent learning and coordination within Toyota Research Institute’s Accelerated Materials Design and Discovery program.

Compensation

The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location.

Responsibilities

Implement and evaluate algorithms for learning and coordination among agents with different data and computational capabilities.
Prototype methods for sharing information, updating predictive models, and selecting computational tasks.
Design controlled benchmarks comparing coordination strategies with independent‑agent and centralized approaches.
Evaluate model accuracy, communication costs, and computational efficiency using reproducible experimental workflows.
Contribute high‑quality, well‑documented code to an internal codebase and help translate results into internal reports, publications, and/or patent disclosures.

Requirements

Doctoral program in computer science, machine learning, statistics, applied math, electrical engineering, or a related discipline

Skills

Python PyTorch

Data Scientist I - Windreich Department for AI & Human Health Research

Mount Sinai Health System · Posted Nov 5, 2026

Open

New York City, New York, United States +2 more
Entry
2+ years

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Lead data science efforts on the PREVENT pAKI project, developing federated and pooled machine learning models to predict postoperative acute kidney injury across multiple hospitals.

Locations (3)

New York City, New York, United States
Los Angeles, California, United States
Nashville, Tennessee, United States

Responsibilities

Build and curate analytic cohorts from OMOP-mapped and perioperative data warehouse sources
Develop and validate prediction models including gradient-boosted trees and graph neural networks
Implement federated and pooled models across three institutions
Apply causal inference and reinforcement learning to identify sub-phenotypes
Maintain version-controlled, reproducible code and documentation

Requirements

Postdoctoral Appointee - Federated Learning and Foundation Model

Argonne National Laboratory · Posted Nov 3, 2026

Open

Lemont, Illinois, United States
On-site
Full-time
$73K–$121K per year
0–5 years

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A research position focused on optimization and training dynamics for large-scale machine learning, specifically within distributed and federated settings and foundation models.

Compensation

The expected hiring range for this position is $72,879.00-$121,465.00.

Responsibilities

Conducts research on optimization and training dynamics for large-scale machine learning, combining analysis with careful experiments.
Designs, implements and benchmarks new training algorithms for distributed and federated settings, with attention to efficiency, privacy and robustness.
Contributes research software to open-source projects and keeps experiments reproducible.
Publishes and presents results at leading conferences and journals, and contributes to program reports.
Works with collaborators across institutions.

Requirements

Ph.D. (completed within the last 0–5 years) in optimization for machine learning, deep learning theory, or distributed or federated learning
Nice to have: stochastic optimization
Nice to have: heavy-tailed noise
Nice to have: analysis of loss landscapes
Nice to have: generalization

Skills

optimization for machine learning deep learning theory

Senior ML Engineer - Ads ML Infrastructure

Apple · Posted Nov 3, 2026

Open

New York City, New York, United States
On-site
Senior
6+ years

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Design, develop, and build secure and scalable back‑end ML systems for Apple’s Ad Platforms, enabling real‑time and batch inference at scale.

Responsibilities

Design and develop large‑scale, low‑latency ML serving platforms
Optimize model quantization, tensor parallelism, and inference engines (ONNX Runtime, TensorRT, vLLM)
Build and maintain distributed systems for scalable inference (Ray, high‑throughput RPC)
Develop low‑level GPU kernels to maximize hardware utilization
Lead performance tuning and troubleshooting

Requirements

PhD in Computer Science
MS in Computer Science
BS in Computer Science
Nice to have: PhD
Nice to have: MS

Skills

Large‑scale ML serving Model quantization Tensor parallelism Inference optimization Distributed systems GPU kernel development

Data Scientist I - Windreich Department for AI & Human Health Research

Data Scientist I - Windreich Department for AI & Human Health Research

Locations (3)

Compensation

Responsibilities

Requirements

Skills

Accelerated Science Intern, Multi-Agent Learning and Coordination

Compensation

Responsibilities

Requirements

Skills

Data Scientist I - Windreich Department for AI & Human Health Research

Locations (3)

Responsibilities

Requirements

Postdoctoral Appointee - Federated Learning and Foundation Model

Compensation

Responsibilities

Requirements

Skills

Senior ML Engineer - Ads ML Infrastructure

Responsibilities

Requirements

Skills

Benefits

Skills

Benefits