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About This Role
Position Summary
Samsung, a world leader in advanced semiconductor technology, is founded on a simple philosophy – the endless pursuit of excellence will create a better world for all. At Samsung Austin Research and Development Center (SARC) and Advanced Computing Lab (ACL), we are building a center of excellence for Intellectual Property (IP) that is applied to high\-performance computing devices (mobile, automotive, and other custom market segments) consumed by millions of people around the world. Come build with us!Role and Responsibilities
As a Machine Learning Application Developer, you will develop neural rendering applications and machine learning (ML) software that enable efficient execution of AI workloads on Samsung’s premium mobile GPUs.
In this individual contributor role, you will contribute to the development of software solutions that bridge machine learning workloads and GPU hardware capabilities. Working closely with hardware, software, and architecture teams, you will help optimize performance, efficiency, and resource utilization to support next\-generation intelligent computing experiences.
- You help developing and optimizing neural rendering applications, API\-level software, and ML operator implementations, including GEMM, convolution, activations, and related workloads, using Vulkan, OpenGL, and OpenCL to enable efficient execution of ML and graphics workloads on Samsung GPU platforms.
- You analyze software performance and hardware resource utilization to identify bottlenecks and optimize application performance, efficiency, and scalability across a variety of ML workloads.
- You proactively seek collaborations with GPU architects, software engineers, and hardware teams to understand underlying hardware constraints and translate performance insights into optimized software solutions.
- You leverage low\-level performance analysis techniques, including assembly\-level investigation when needed, to help improve execution efficiency and maximize GPU utilization.
- You take initiatives on moderate\-to\-complex projects and help advance best practices and methodologies by staying current with the latest advancements in machine learning, neural rendering, and GPU technologies.
Skills and Qualifications
- 3\+ years of experience with a Bachelor's Degree in Computer Science, Computer Engineering, or comparable field, or 2\+ years of experience with a Master’s Degree, or Ph.D.
- Strong programming skills in C, C\+\+, and Python.
- Proficiency with API\-level programming using in Vulkan, OpenGL, OpenCL, and machine learning frameworks such as PyTorch and TensorFlow.
- Understanding of GPU hardware architecture and experience with low\-level performance profiling, analysis, and optimization.
- Hands\-on experience developing neural rendering applications at the API level.
- Working knowledge of machine learning operators and workloads, including GEMM, convolution, activations, and related computational kernels.
- Ability to analyze hardware resource constraints and bottlenecks and develop software optimizations that improve performance and efficiency.
- Working knowledge of assembly\-level analysis, debugging, or optimization is preferred.
- Strong analytical and problem\-solving skills, with the ability to identify bottlenecks and propose data\-driven solutions.
- Excellent communication and collaboration skills, with the ability to navigate ambiguity in a fast\-paced, global team environment.
Our Team
The GPU Architect team delivers whole system architecture\-level design for Samsung’s current and next\-generation mobile GPUs. Our work spans a wide range of disciplines—from early\-stage architectural exploration and research in graphics and machine learning, to shaping the GPU innovation roadmap, defining new features, building models, and delivering microarchitecture design and programming. Our Xclipse GPU is the first mobile GPU with ray tracing technology that enables console\-level graphics for Samsung Galaxy smartphones.
Joining our team means collaborating with talented engineers from diverse technical backgrounds across a global organization. Here you’ll contribute to the roadmap for next\-generation technologies, broaden your expertise, and solve impactful challenges in a supportive environment that values collaboration, continuous learning, and growth.
Total Rewards
At Samsung – SARC/ACL, 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 $124,000 and $208,400\. Your actual base pay will depend on variables that may include your education skills, qualifications, experience, and work location.
Samsung employees have access to benefits including: medical, dental, vision, life insurance, 401(k), onsite lunch, employee purchase program, tuition assistance (after 6 months), paid time off, student loan program, wellness incentives, and many more. In addition, regular full\-time employees (salaried or hourly) are eligible for MBO bonus compensation, based on company, division, and individual performance.
Additionally, this role might be eligible to participate in long term incentive plan and relocation.
This is an exempt position, which is not eligible for overtime pay under the Fair Labor Standards Act (FLSA).
U.S. Export Control
This position requires the ability to access information subject to U.S. export control restrictions. Applicants must have the ability to access export\-controlled information or be eligible to receive a government authorization to access export\-controlled information.
Trade Secrets
By submitting an application, you \[applicant] agree\[s] not to disclose to Samsung, or induce Samsung to use, any confidential or proprietary information (including trade secrets) belonging to any current or previous employer or other person or entity.
\#SARC \#ACL
- Samsung Electronics America, Inc. and its subsidiaries are 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.
Salary Context
This $124K-$208K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Samsung Electronics, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($166K) sits 10% below the category median. Disclosed range: $124K to $208K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Samsung Electronics AI Hiring
Samsung Electronics has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Mountain View, CA, US, San Jose, CA, US. Compensation range: $208K - $215K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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