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About This Role
Position Summary
Headquartered in Plano, TX., Samsung Electronics America, Inc. (SEA) is a leader in mobile technologies, consumer electronics, home appliances and enterprise solutions. From our humble beginnings to our position today as a tech leader, our passion for innovation has been the common thread throughout our history. We’ve grown into one of THE most recognized global brands. We consider ourselves “relentless pioneers” that push boundaries and defy barriers. The company pushes beyond the limits of today’s technology to provide groundbreaking connected experiences across its large portfolio of products and services, including mobile devices, home appliances, home entertainment, 5G networks, and digital displays. As EPA’s ENERGY STAR® Corporate Commitment Partner, SEA is dedicated to making a positive impact on the environment through its eco\-conscious products, practices, and operations.
People \| Excellence \| Change \| Integrity \| Co\-Prosperity
Role and Responsibilities
Samsung Electronics is seeking an innovative, highly strategic technologist and operational leader to fill the role of Director of B2B Data Platforms \& AI Transformation. This pivotal position sits at the intersection of enterprise data infrastructure, business intelligence, and advanced AI automation systems.
The successful candidate will own the strategic direction, technical governance, maintenance, and end\-to\-end optimization of our core enterprise data ecosystems (Claude, Salesforce, Tableau BI platforms, and multidimensional data cubes/repositories). Concurrently, this leader will aggressively spearhead our AI transformation roadmap, deploying agentic frameworks and workflows to drive unprecedented operational efficiencies, remove manual reporting bottlenecks, and automate cross\-functional metrics across regional and global operations.
Strategic Objective: Transform B2B’s data estate from a series of passive storage and reporting layers into a highly integrated, intelligent ecosystem that leverages AI automation to provide proactive insights directly into executive reporting pipelines and SCM workflows.
Core Responsibilities
*1\. AI Transformation \& Intelligent Automation*
- Drive Efficiency Roadmaps: Architect and execute an enterprise\-wide AI transformation strategy focused on automating manual legacy data reporting systems and compressing data\-to\-insights cycles.
- Agentic Frameworks \& Workflows: Evaluate, design, deploy, and govern intelligent AI agents (including Salesforce Agentforce tools and customized LLM layers) to manage automated data verification, conversational analytics, and anomaly detection.
- Operational Modernization: Modernize traditional reporting mechanisms across divisions by establishing automated workflows that deliver predictive insights natively within standard operational software.
*2\. Enterprise Data Ecosystem \& Infrastructure Management*
- Salesforce Systems Management: Oversee the structural integrity, data hygiene, and optimization of the Salesforce CRM platform as a primary enterprise customer and sales data engine.
- Advanced Analytics \& Business Intelligence: Serve as the executive product owner for Tableau (Cloud and Server environments), establishing enterprise semantic layers, unified metadata definitions, and master data guidelines.
- Data Repository \& Cube Optimization: Supervise the maintenance, performance tuning, and structural scaling of multidimensional data repositories (OLAP cubes, data lakes, and modern lakehouse frameworks) to ensure sub\-second query performance for highly complex reporting dashboards.
*3\. Change Management \& Cross\-Functional Leadership*
- Matrix Stakeholder Alignment: Partner extensively with business owners across Sales, Supply Chain Management (SCM), Revenue Operations, and Finance to align analytical models and standardize data governance protocols.
- Data Culture Cultivation: Lead technical change management frameworks to upskill business units, transitioning standard business analysts away from manual data preparation toward AI\-assisted self\-service analytical pipelines.
*4\. Team Leadership \& Talent Engineering*
- Manage Talent: Lead, recruit, and mentor a high\-performing team consisting of senior data engineers, BI architects, platform managers, and AI workflow optimization specialists.
- Culture of Agility: Foster a culture of technical agility, continuous learning, and strict data privacy compliance across all AI operations.
Skills and Qualifications
Required Qualifications \& Competencies
*Experience \& Education*
- Professional Experience: Minimum of 10–12\+ years of progressive technical experience in Data Engineering, Enterprise Platform Architecture, or Business Intelligence.
- Leadership Record: 5\+ years of dedicated team leadership, managing multi\-disciplinary engineering or data platforms teams within a large, multi\-national matrix organization.
- Education: Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or a related quantitative field. An MBA paired with a technical background is highly desirable.
*Technical \& Functional Competencies*
- Ecosystem Proficiency: Demonstrated architectural\-level command over Salesforce CRM and Tableau BI ecosystem deployment, security protocols, and API scaling.
- Data Architecture Expertise: Hands\-on experience scaling enterprise data repositories
- AI \& ML Integration: Thorough, up\-to\-date knowledge of enterprise AI integration paradigms, metadata orchestration, LLM context windows, semantic caching, and building closed\-loop agentic automations.
*Core Leadership Traits*
- Executive Communication: Outstanding capabilities in translating intricate, highly technical backend architecture details into clear, value\-oriented business impact metrics for C\-suite and executive vice president stakeholders.
- Strategic Problem\-Solving: Strong analytical and change\-management focus, capable of dissolving historical data silos and harmonizing disparate global business groups under standard data strategies.
\#LI\-RL2
Life @ Samsung \- https://www.samsung.com/us/careers/life\-at\-samsung/
Benefits @ Samsung \- https://www.samsung.com/us/careers/benefits/
Regular full\-time employees (salaried or hourly) have access to benefits including: Medical, Dental, Vision, Life Insurance, 401(k), Employee Purchase Program, Tuition Assistance (after 6 months), Paid Time Off, Student Loan Program (after 6 months), 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 on company, division, and individual performance.
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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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Samsung Electronics AI Hiring
Samsung Electronics has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Plano, TX, US, San Jose, CA, US. Compensation range: $297K - $297K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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