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About This Role
Lab Summary:
Our mission is to empower people to live healthier lives by leveraging our wearables, smartphones, medical devices, AI, and health services. We research, develop, and commercialize innovative digital health products and solutions by bringing together the full capabilities of our team, including product strategy and management, clinical expertise, partnerships, UI/UX design, usability research, clinical research and validation, software development, AI / Gen AI, and regulatory. Millions of Samsung users' lives around the world have been touched by our products, including the Samsung HeartWise for cardiac rehabilitation and the Samsung ECG Monitor App. Samsung's unique advantage in the consumer electronics market and growing focus on Digital Health and AI / Gen AI provides exciting technical challenges and rewarding career experience. Join us!
Position Summary:
Samsung Research America Digital Health Team is looking for an outstanding Ph.D.-level Senior AI Research Engineer with solid ML/AI technology background and digital health-related research project experiences in digital biomarker, physiological signal processing and deep learning. You need to have a strong track-record of success in academic and/or industrial settings, with the ability to scale algorithms and models to large, real-world datasets.
You will play a key role in delivering innovation in digital health domain, creating technologies and intellectual properties, deploying and validating novel algorithms, and transferring mature outcomes to production and scientific publications in top-tier venues. We expect candidates who have solid data analytics skills and project experiences in areas such as health foundation models pre and post training, wearable computing, and algorithm development for health applications. Your work will significantly benefit real-world wearable users, seniors, physicians and care givers. Samsung's unique advantage in the consumer electronics market and growing focus on digital health will provide you with exciting technical challenges and a rewarding career experience.
Position Responsibilities:
- Research and develop novel AI/ML algorithms and foundation models for physiological signal processing, sensor fusion, and digital biomarkers in wearable health applications, leveraging self-supervised learning, transformer models, and computer vision models on very large-scale real-world user datasets
- Design and implement deep learning and statistical models to analyze multi-modal biosignals (e.g., EEG, ECG, PPG, motion data) in real-time and batch settings
- Scale algorithms to handle large, multi-terabyte datasets from wearables and biomedical sensors on cloud platforms (AWS, GCP, Azure) while maintaining algorithmic efficiency
- Collaborate with hardware and software engineers to integrate AI models into wearable platforms, ensuring efficient on-device inference
- Conduct feasibility studies, benchmark AI-driven features, and refine models for accuracy and scalability across diverse populations
- Work with clinical and regulatory teams to ensure model robustness, validation, and compliance with healthcare standards
- Stay up-to-date with the latest advancements in digital health, computational neuroscience, and edge AI to drive innovation in wearables
- Generate patents and scientific research papers for top-tier publications
Required Skills:
- PhD in AI, Machine Learning, Biomedical Engineering, Computer Science, or a related field or equivalent combination of education, training, and experience
- 3+ years of experience in data analytics and project experiences in areas such as health foundation models pre and post training, wearable computing, and algorithm development for health applications
- 3+ years of experience in ML/DL frameworks (TensorFlow, PyTorch) and programming languages (Python, C++)
- Track record of publishing in high-impact AI/ML venues (ICML, ICLR, NeurIPS, etc.) or top-tier journals (Nature, Science, etc.)
- Experience with deep learning on time-series data and sequence models (LSTMs, attention mechanisms, temporal convolutions)
- Experience with time-series analysis, signal processing, and sensor fusion for wearable or biomedical applications
- Familiarity with biomedical sensing modalities (EEG, ECG, PPG, SpO2, etc.) and data-driven health insights
- Experience designing and optimizing algorithms to work efficiently with large-scale datasets and distributed computing environments
- Experience in developing PoCs and exploratory research projects in emerging sensing technologies
Special Attributes:
- Understanding of clinical workflows and healthcare data privacy considerations (HIPAA, FDA, MDR, etc.)
- Knowledge of wearable hardware constraints, power optimization, and real-time processing
- Familiarity with on-device ML optimization and model compression techniques
- Experience scaling algorithms and model training on cloud compute infrastructure (AWS, GCP, Azure) or distributed computing environments
Additional Information
Disclosure of Trade Secrets
Samsung has a strict policy on trade secrets. In applying to Samsung and progressing through the recruitment process, you must not disclose any trade secrets of a current or previous employer.
Essential Job Functions
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, and frequently operate standard office equipment, such as telephones and computers.
Samsung Research America is committed to complying with all Federal, State and local laws related to the employment of qualified individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact the recruiter or email [email protected].
Equal Employment Opportunity
At Samsung, we believe that innovation and growth are driven by an inclusive culture and a diverse workforce. We aim to create a global team where everyone belongs and has equal opportunities, inspiring our talent to be their true selves. Together, we are building a better tomorrow for our customers, partners, and communities.
Samsung Research America is committed to employing a diverse workforce, and provide Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, status as a qualified individual with a disability, or any other characteristic protected by law.
For more information regarding protection from discrimination under Federal law for applicants and employees, please refer to this link: Pay Transparency
Salary Context
This $184K-$253K range is above the 75th percentile for Research Engineer roles in our dataset (median: $165K across 57 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,079 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Samsung Research America, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 457 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($219K) sits 13% below the category median. Disclosed range: $184K to $253K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Samsung Research America AI Hiring
Samsung Research America has 2 open AI roles right now. They're hiring across AI Product Manager, Research Engineer. Based in Mountain View, CA, US. Compensation range: $224K - $253K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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