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About This Role
Position Summary
Position Summary
Samsung Ads is a fast\-growing advanced advertising technology company that empowers advertisers to connect with audiences across Samsung devices through digital media. Leveraging the industry's most comprehensive first\-party data, we are building the world's smartest advertising platform. As part of the global Samsung ecosystem, we tackle large\-scale, complex projects alongside stakeholders and teams around the world.
We have built a world\-class organization rooted in entrepreneurship and collaboration. At Samsung Ads, you'll discover just how fast you can grow, how much you can achieve, and how far you can go. We thrive on solving hard problems, breaking new ground, and enjoying the journey along the way.
Machine learning is at the heart of modern advertising, and Samsung Ads is no exception. We are actively exploring cutting\-edge ML techniques to enhance existing systems, build new products, and unlock new revenue streams. As a Machine Learning Platform Engineer on the Platform Intelligence (PI) team, you will have access to Samsung's unique proprietary data to help develop and deploy large\-scale machine learning products with real\-world impact. You will work alongside experienced engineers and world\-class researchers on exciting projects using state\-of\-the\-art technologies. You will be welcomed by a culture of continuous learning, mentorship, and a creative work atmosphere. This is an excellent opportunity to accelerate your career by contributing to cutting\-edge machine learning products within a rapidly growing team.
Location: Mountain View, CARole and ResponsibilitiesResponsibilities* Develop and maintain machine learning platform components that support large\-scale model training pipelines and batch prediction systems.
- Contribute to building a world\-class ML platform tailored for Samsung's ML\-based advertising business.
- Build and improve CI/CD pipelines, data workflows, and monitoring systems to enhance platform reliability and efficiency.
- Assist in researching and evaluating new machine learning platform technologies through prototypes and proof\-of\-concepts.
- Collaborate with internal ML teams (e.g., ML Serving and ML Engineering) to improve codebase quality and product health.
- Work with cross\-functional partner teams to support the delivery of new ML features and solutions.
- Troubleshoot issues, optimize system performance, and contribute to engineering best practices.
- Learn quickly and adapt to a fast\-paced working environment.
Experience Requirements* 3\-4 years of industry experience with a Bachelor's degree, or 2 years of industry experience with a Master's degree in Computer Science or related fields such as Statistics, Data Science, Technology, Engineering, or Mathematics.
- Solid programming skills in Python, with familiarity in SQL and databases.
- Foundational knowledge of machine learning concepts and hands\-on experience with at least one ML framework (e.g., TensorFlow, PyTorch, or Spark ML).
- Familiarity with big data tools and concepts (e.g., Spark, Kafka, or similar technologies).
- Basic understanding of containerization (Docker) and orchestration (Kubernetes).
- Exposure to CI/CD pipelines, version control (Git), and software engineering principles.
- Understanding of data structures and algorithms.
- Good communication skills and ability to collaborate effectively in a team environment.
- Eagerness to learn new technologies and adapt to a fast\-paced environment.
Skills and QualificationsPreferred Experience Requirements* Experience with cloud platforms, particularly Amazon Web Services (AWS).
- Familiarity with Infrastructure as Code (Terraform) or workflow orchestration tools (Airflow).
- Exposure to monitoring and alerting tools such as Prometheus or Grafana.
- Experience with Snowflake or similar data warehouse technologies.
- Interest in or exposure to the advertising industry and real\-time bidding (RTB) ecosystem.
- Personal projects or contributions to open\-source ML or data engineering projects.
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 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.
Reasonable Accommodations for Qualified Individuals with Disabilities During the Application Process
Samsung Electronics America is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application process. If you have a disability and require a reasonable accommodation in order to participate in the application process, please contact our Reasonable Accommodation Team (855\-557\-3247\) or SEA\_Accommodations\_Ext@sea.samsung.com for assistance. This number is for accommodation requests only and is not intended for general employment inquiries.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Samsung Electronics AI Hiring
Samsung Electronics has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Mountain View, CA, US, Plano, TX, US, New York, NY, US. Compensation range: $72K - $329K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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