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About This Role
Position Summary
Samsung Ads focuses on enabling brands to connect with Samsung audiences across
all devices. As an international company, Engineers at Samsung work on big complex
projects with stakeholders and teams located around the globe.
Our purpose is to deliver unparalleled results for our customers. Samsung Ads uniquely
transforms the advertising landscape by using comprehensive data to build the world’s
most intelligent connected audience platform. We deliver on Samsung Electronics’
51-year commitment to excellence through smart, easy, effective advertising solutions to
make advanced video advertising work. Role and Responsibilities
What you will do
As a Senior GenAI Engineer, you will be the hands-on driving force behind AI-powered productivity and automation across Samsung Ads. Reporting to the IT Director, you will work directly with non-technical teams across every department to identify, build, deploy, and scale Generative AI solutions that fundamentally improve how people work.
You will partner closely with a dedicated Product Owner and the Principal Architect from Engineering to shape and execute our GenAI enablement roadmap. While Engineering builds core platform capabilities, your focus is on the last mile — putting powerful, safe, and practical AI tools directly into the hands of business users.
A critical dimension of this role is designing and orchestrating agentic AI workflows — multi-step, autonomous processes where AI agents plan, reason, use tools, and execute complex tasks with minimal human intervention. As enterprises shift from simple prompt-based AI interactions toward agentic architectures, you will be responsible for identifying where autonomous AI workflows can replace manual, multi-step business processes — and building them safely within Samsung's governance framework.
You are expected to be opinionated, delivery-oriented, and security-conscious in everything you do.
Key Responsibilities
- Design, build, and deploy GenAI-powered automation and productivity solutions for business teams across Sales, Marketing, Finance, HR, Legal, and Operations.
- Architect and implement complex agentic workflows that enable AI agents to autonomously handle multi-step business processes — including task decomposition, tool orchestration, decision-making loops, and human-in-the-loop checkpoints where required.
- Design and maintain multi-agent orchestration patterns , where specialized AI agents collaborate on complex tasks such as document analysis, research synthesis, data transformation, and cross-system process automation.
- Serve as the primary hands-on technical resource for our AI tooling ecosystem, including AWS Bedrock, Claude, AnythingLLM , and our homegrown GenAI platform built on open-source tools.
- Identify high-impact use cases through direct engagement with business stakeholders, translating pain points into practical AI-driven workflows — with a specific focus on processes that benefit from autonomous, adaptive execution rather than simple prompt-response interactions .
- Build and maintain custom AI agents, prompt chains, RAG pipelines, and integrations tailored to departmental needs.
- Integrate agentic AI workflows with existing enterprise applications and business systems (e.g., CRM, HRIS, finance tools, collaboration platforms) to enable end-to-end process automation across departments.
- Define guardrails, escalation paths, and human oversight mechanisms for agentic workflows to ensure autonomous AI actions remain safe, auditable, and aligned with Samsung's internal policies.
- Develop evaluation frameworks for AI tools and agentic systems based on production requirements — including accuracy benchmarks, latency, cost-per-task, and compliance criteria — not vendor demos.
- Evaluate and recommend new AI tools and vendors in collaboration with the Architecture team, including hands-on proof-of-concepts to support build-vs-buy decisions.
- Ensure every AI solution deployed adheres to Samsung's internal security and privacy policies , including data classification requirements mandated by HQ (South Korea).
- Proactively assess data exposure risks of any AI tool or workflow before deployment and maintain a clear inventory of all AI tools, their data flows, and classification levels.
- Define and track KPIs for every AI initiative: workflows automated, hours saved, adoption rates, and user satisfaction.
- Develop training sessions, documentation, and self-service resources to accelerate AI adoption among non-technical users. (Nice-to-have)
Required Skills And Experience
- 3+ years of hands-on experience with Generative AI tools and platforms, with a proven track record of delivering successful productivity or automation projects adopted by real users.
- 5+ years of experience in IT, automation, systems engineering, or a related technical role.
- Proven experience delivering at least one end-to-end production deployment of a GenAI or agentic AI system in an enterprise environment — not just prototypes or POCs, but solutions actively used by business teams.
- Strong hands-on proficiency with AWS Bedrock , Anthropic Claude (API, Claude Code, prompt engineering), and open-source AI tooling.
- Demonstrated experience designing agentic AI workflows — including multi-agent orchestration, tool-use patterns, autonomous task execution, and deterministic execution patterns suitable for enterprise environments.
- Experience with agentic AI frameworks and protocols such as Model Context Protocol (MCP), LangGraph, Claude Agent SDK, Amazon Bedrock Agents, CrewAI , or equivalent orchestration tools for building autonomous, multi-step AI workflows.
- Understanding of agentic design patterns : reflection, planning, tool use, multi-agent collaboration, human-in-the-loop checkpoints, and error recovery strategies.
- Working experience with RAG architectures , vector databases, embeddings, and self-hosted AI knowledge base platforms (e.g., AnythingLLM).
- Proficiency in Python for scripting, automation, and AI tool integration.
- Strong understanding of data classification frameworks , LLM-specific risks (data leakage, prompt injection, hallucination, unintended autonomous actions), and corporate security policies in a multinational enterprise environment.
- Experience working with non-technical stakeholders and translating business needs into technical solutions.
- A solution-oriented, opinionated approach — you have a clear point of view on what works, what scales, and what's hype.
- Highly organized and able to manage multiple priorities in a fast-paced environment.
- Strong communication skills and the ability to convey AI capabilities and limitations to any audience.
- Bachelor's degree in Computer Science, Information Technology, or related field (or equivalent practical experience).
Nice-to-Have
- Experience with Model Context Protocol (MCP) for building tool-integrated AI agents at scale.
- Experience integrating AI workflows with enterprise business systems (Salesforce, SAP, ServiceNow, Workday, or similar).
- Background in Ad Tech or experience working in a digital advertising environment.
- Familiarity with change management frameworks and delivering training/enablement programs at scale.
- Experience navigating multinational corporate governance structures (especially APAC-headquartered companies).
- Relevant certifications: AWS AI Practitioner, AWS ML Specialty, Anthropic Claude certification , or equivalent.
- Experience building or managing AI Centers of Excellence or similar enablement functions.
- Fluency in Korean.
Skills and Qualifications
Why This Role Is Different
Unlike GenAI roles buried inside engineering orgs building internal tooling, this position puts you at the center of a company-wide AI transformation. You will have direct visibility with business leadership, a mandate to move fast, and the autonomy to shape how an entire organization works with AI.
You won't be optimizing model benchmarks — you'll be measured on business impact . Workflows automated. Hours saved. Teams empowered. Processes reinvented. If you've been looking for a role where Generative AI meets real-world business problems at scale, and where you have the freedom to be opinionated about how to solve them, this is it.
If you're interested in joining a growing team building an outstanding, world-class advertising organization with a relentless focus on design and customer experience, you've come to the right place!
Salary Range Pay Transparency: Compensation for this role, for candidates based in New York, NY is expected to be between Base range: $170,000 ~ $210,000. Actual pay will be determined considering factors such as relevant skills and experience, and comparison to other employees in the role. 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.
- 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 [email protected] for assistance. This number is for accommodation requests only and is not intended for general employment inquiries.
Salary Context
This $170K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $170K to $210K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Samsung Electronics AI Hiring
Samsung Electronics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $210K - $210K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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