Machine Learning Research Scientist - Health AIML

Apple Inc.
Seattle, WA, United States
2 months ago
Apply on www.jobmonkeyjobs.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$171,600.0
Working hours
Regular working hours

Tech stack

Artificial Intelligence Software Debugging Machine Learning Tensorflow Pytorch Large Language Models Deep Learning Information Technology Artificial Intelligence Markup Language (AIML)

Job description

We’re developing next-generation multimodal models to create intelligent health and fitness experiences. This role requires someone with strong expertise in large multimodal models to work at the intersection of AI and health to build foundational models that scale to billions of users worldwide. Your work will shape the future of health and fitness technologies at Apple.

We are looking for a research lead to guide multimodality research. You will lead the development of foundational technology that enables models to understand health and fitness data.

Responsibilities

Lead research into health and fitness representation models and multimodal models.

Design, prototype and scale up new architectures to improve model intelligence.

Execute and analyze experiments autonomously and collaboratively.

Study, debug, and optimize model performance and computational performance.

Contribute to training and inference infrastructure.

Requirements

PhD in Computer Science/Engineering, Machine Learning, Statistics, Mathematics or related field.

Industry work experience.

Experience landing contributions to major LLM training runs.

Proven track record of publishing SOTA.

Strong skills with deep learning frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications

Experience in training and evaluating multimodal models.

Understand of time-series modeling, self-supervised learning, and cross-modal training.

Ability to thoroughly evaluate and improve deep learning architectures in a self-directed fashion.

Motivated by safely deploying LLMs in the health and fitness space.

Benefits & conditions

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 $171,600 and $302,200, 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.

About the company

The Health AIML team is at the forefront of machine learning and health science at Apple. We are a close-knit team of research scientists, software engineers and machine learning engineers passionate about delivering innovative technologies that impact millions of users. We are looking for a Machine Learning Research Scientist with strong dedication to solving real-world problems in health and fitness that enrich our customers’ lives.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.jobmonkeyjobs.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

8:51 min

Addressing technical strategies and interdisciplinary computing dynamics

Noah Weber · LIVE

1:25 min

Distinguishing artificial intelligence from deep learning

Sam Witteveen · Coffee With Developers

1:39 min

Fundamentals of tensors and the TensorFlow library

Håkan Silfvernagel · LIVE

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

3:53 min

Architecting machine learning projects with the PAI platform

Qiyang Duan · LIVE

3:11 min

Applying artificial intelligence frameworks for social good initiatives

Toju Duke · World Congress 2022

Videos

See all

Related articles

See all