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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
Samsung Electronics America is seeking a highly versatile and analytically driven AI Analytics leader to join our Data \& AI Analytics organization. This is a unique, high\-impact business\-side role designed for a professional who can operate across the full data spectrum \- deriving sharp business insights from data, engineering the scalable infrastructure that powers those insights, and architecting the enterprise data platform that ensures everything is governed, trusted, and future\-proof.
You will work within a fully GCP\-native environment, leveraging the breadth of Google Cloud's data and AI services \- from BigQuery and Dataflow to Vertex AI and Gemini \- to deliver end\-to\-end data capabilities across SEA's consumer electronics, eCommerce, and B2B business lines. You will also be a hands\-on contributor to SEA's growing AI and agentic development practice, building intelligent, automated workflows that amplify the value of data across the organization.
As a Senior Manager, you will directly manage a team of 3\-4 analysts and/or contractors, navigate Samsung's matrixed global organization to align stakeholders across regional and HQ boundaries, and drive data and AI initiatives from ideation to business impact. You bring 8\-15 years of combined experience across data analysis, data engineering, and data architecture, and you thrive where technical depth meets business strategy and people leadership.
Role and Responsibilities
*Data Analysis \& Business Insights*
- Analytics Ownership: Design and execute end\-to\-end analyses on large, complex datasets to answer strategic business questions across consumer electronics, mobile, home appliances, eCommerce, and B2B segments; translate findings into clear, actionable recommendations for senior stakeholders.
- Dashboards \& Reporting: Build, own, and continuously improve interactive dashboards and self\-serve reporting solutions in Looker and Looker Studio; define metrics, KPIs, and business logic in alignment with stakeholder needs.
- Data Storytelling: Communicate complex analytical findings through compelling narratives and visualizations tailored to both technical and non\-technical audiences including executive leadership.
- Data Quality Stewardship: Monitor, validate, and enforce data quality across analytical datasets; partner with Engineering to resolve root cause issues and establish data SLA standards.
*Data Engineering \& Pipeline Development*
- Pipeline Design \& Development: Design, build, and maintain scalable batch and real\-time ELT/ETL data pipelines using Google Dataflow (Apache Beam), Cloud Composer (Apache Airflow), Pub/Sub, and dbt; ensure pipelines are performant, observable, testable, and production\-grade.
- BigQuery Data Modeling: Develop and maintain BigQuery datasets, tables, and data models; apply dimensional modeling, partitioning, clustering, and cost\-optimization best practices to serve both analytical and operational workloads at SEA scale.
- Data Ingestion \& Integration: Integrate structured and unstructured data from diverse sources \- APIs, operational databases, event streams, third\-party SaaS platforms, and IoT/SmartThings device data \- into SEA's centralized GCP data platform.
- Infrastructure as Code: Manage GCP data infrastructure using Terraform; enforce IaC principles to ensure reproducibility, version control, and environment consistency across development, staging, and production.
- Observability \& Reliability: Implement data quality checks, pipeline SLA monitoring, and alerting using Cloud Monitoring and dbt tests; own pipeline reliability and participate in on\-call escalation for critical data flows.
*Data Architecture \& Governance*
- Enterprise Data Architecture: Design and govern the end\-to\-end data architecture for SEA on GCP \- spanning ingestion, storage, transformation, serving, and AI layers \- ensuring alignment with business strategy, scalability requirements, and global Samsung standards.
- Data Mesh \& Governance: Lead the design and implementation of data mesh principles at SEA using GCP Dataplex \- defining data domains, establishing data product ownership, implementing federated governance, and enabling self\-serve data access across business units.
- Data Catalog \& Lineage: Own SEA's data catalog and metadata strategy using Google Data Catalog and Dataplex; define tagging taxonomy, lineage capture, PII classification, and business glossary standards to drive data discoverability, trust, and compliance.
- Security \& Compliance Architecture: Architect and enforce data security controls across the GCP stack: IAM, VPC Service Controls, column\-level security, dynamic data masking, and encryption; ensure architecture meets CCPA, GDPR, SOX, and Samsung global data compliance requirements.
- Architecture Standards: Define and enforce architectural standards, design patterns, and best practices for all data engineering and analytics development at SEA; conduct architecture reviews and provide technical guidance to cross\-functional engineering teams.
*AI, Generative AI \& Agentic Development*
- AI\-Ready Data Platform: Design the foundational architecture for AI and generative AI workloads on GCP \- including Vertex AI Feature Store topology, vector database design (Vertex AI Vector Search, AlloyDB pgvector), and AI data pipeline patterns that support RAG, fine\-tuning, and model serving at scale.
- Agentic Workflow Development: Build and deploy AI agents and multi\-agent systems using LangChain, LangGraph, and Google Agent Development Kit (ADK) that combine LLM reasoning with structured data retrieval, tool use, and automated decision\-making across SEA data workflows.
- RAG Pipeline Engineering: Design and implement Retrieval\-Augmented Generation (RAG) pipelines connecting BigQuery and Vertex AI Vector Search with Gemini/PaLM APIs to power intelligent internal copilots, natural language data querying, and automated insight generation.
- Generative AI Integration: Integrate Vertex AI Generative AI and Gemini APIs directly into analytics and data pipelines \- including automated anomaly summarization, stakeholder report generation, and AI\-assisted data discovery capabilities.
- MLOps Support: Support Data Science teams by building feature engineering pipelines, managing data feeds to Vertex AI Feature Store, maintaining model input/output schemas, and contributing to Vertex AI Pipelines for end\-to\-end ML workflow automation.
- Prompt Engineering \& Evaluation: Apply prompt engineering best practices; develop evaluation frameworks to assess LLM output quality, agent reliability, and RAG retrieval accuracy in production environments.
*People Management \& Global Stakeholder Navigation*
- Team Leadership: Directly manage a team of 3\-4 analysts and data professionals; set clear goals and priorities, conduct regular 1:1s, provide ongoing coaching and performance development, and hold the team accountable to delivery standards and quality benchmarks.
- Contractor Management: Oversee and manage external contractors and vendor resources supporting data and AI initiatives; define scopes of work, manage deliverables and timelines, evaluate performance, and ensure contractor output meets SEA quality and security standards.
- Global Organization Navigation: Navigate Samsung's complex, matrixed global organization \- building trusted relationships with counterparts across SEA business units, Samsung Electronics HQ in Korea, and regional affiliates; effectively align stakeholders across time zones, cultures, and organizational layers to drive shared data and AI priorities.
- Influence Without Authority: Drive adoption of data\-driven decision\-making and AI\-powered workflows across business units where you do not have direct authority; build coalitions, manage competing priorities diplomatically, and land initiatives through influence and partnership.
- Cross\-functional Partnership: Act as the senior Data \& AI partner for assigned SEA business lines; proactively identify opportunities to leverage data and AI to solve business problems, capture revenue, reduce cost, or improve operational efficiency \- and translate those opportunities into funded, prioritized work.
- Stakeholder Communication: Communicate data and AI strategy, progress, and outcomes clearly to audiences ranging from individual contributors to VP\-level business leaders; translate technical complexity into business\-relevant language and compelling narratives.
- Documentation \& Knowledge Management: Author and maintain architecture decision records (ADRs), data contracts, analytical methodology documentation, and team playbooks; build a culture of documentation and institutional knowledge retention within the team.
Skills and Qualifications
REQUIRED QUALIFICATIONS
- Bachelor's degree in Computer Science, Data Science, Data Engineering, Information Systems, Statistics, Mathematics, or a related quantitative field.
- 8\-15 years of combined hands\-on experience spanning data analysis, data engineering, and data architecture in a production, cloud\-native environment.
Deep expertise in Google BigQuery * \- advanced SQL, query optimization, partitioning/clustering, dataset design, cost governance, and cross\-project topology.
- Proficiency in Python for data engineering, pipeline development, data manipulation, and automation scripting.
Hands\-on experience with GCP data pipeline services * \- including at least three of: Google Dataflow, Cloud Composer (Airflow), Pub/Sub, Dataproc, Cloud Storage, or Cloud Functions.
- Strong experience with dbt (data build tool) for data transformation, modeling, testing, and analytics engineering.
- Experience with Looker and/or Looker Studio for dashboard development, semantic data modeling, and self\-serve analytics.
Demonstrated experience with GCP data governance tooling \- Google Dataplex, Data Catalog, or equivalent * \- for metadata management, data lineage, and federated governance.
- Experience with Terraform or equivalent Infrastructure as Code tools for managing cloud data infrastructure.
Hands\-on experience integrating AI/ML APIs into data workflows * \- including calling Vertex AI, Gemini, or equivalent LLM APIs as part of automated pipelines or analytical tools.
- Working knowledge of AI agent frameworks (LangChain, LangGraph, Google ADK, or CrewAI) and the ability to build or extend agentic data workflows with tool use and RAG capabilities.
Understanding of RAG architecture * \- vector embeddings, semantic retrieval, chunking strategies, and evaluation.
- Strong understanding of data modeling paradigms: relational, dimensional, and NoSQL; ability to select and apply the right model to the right problem.
- Deep knowledge of cloud data security: IAM, VPC Service Controls, column\-level security, data masking, encryption, and regulatory compliance (CCPA, GDPR, SOX).
Experience directly managing or leading a team of 2 or more analysts, engineers, or data professionals * \- including setting goals, conducting performance reviews, and developing talent.
Experience managing external contractors or vendor resources * \- including scoping work, managing deliverables, and ensuring quality and compliance.
\#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.
\* Please visit Samsung membership to see Privacy Policy, which defaults according to your location. You can change Country/Language at the bottom of the page.
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\[email protected] 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. Senior-level AI roles across all categories have a median of $230,000.
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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