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About This Role
Position Summary
About Samsung Ads
Samsung Ads is an advanced advertising platform where advertisers find and connect with audiences across over 100M Samsung Households around the world. Samsung Ads delivers high\-quality audience targeting powered by three key components: first\-party audience data at scale, world\-class data science, and brand\-safe cross\-device ad inventory. Using our data, insights, and scale, we help advertisers reach consumers across CTV, our native apps, mobile, retail display and more. With Samsung Ads, advertisers can buy the way they want, reach who they need, and prove business results.
Our purpose is to deliver unparalleled results for our customers. Samsung Ads is uniquely positioned to perform in the evolving advertising landscape. We deliver on Samsung Electronics’ 50\+ year commitment to excellence through smart, easy, effective advertising solutions to make advanced advertising work.
We are proud to have built a world\-class organization, grounded in an entrepreneurial and collaborative spirit. Working at Samsung Ads offers one of the best environments in the industry to learn just how fast you can grow, how much you can achieve, and how good you can be. We thrive on problem\-solving, breaking new ground, and enjoying every part of the journey.Role and Responsibilities
The Senior Manager, Paid Media will be responsible for co\-developing and executing data\-driven paid media strategies to grow Samsung Ads' B2B customer base and revenue across paid channels. They will make an impactful contribution to our growing growth marketing division within the Samsung Ads Marketing organization, helping to build a program responsible for driving incremental business opportunities, generating high\-quality leads, accelerating pipeline, and contributing to revenue growth.
Reporting into the Director of Demand Generation, the Paid Media Senior Manager will co\-own performance marketing strategy and execution across paid search, paid social, programmatic display, and content syndication. The right candidate will drive engagement and lead generation across these channels through a cross\-channel approach that attracts, educates, and nurtures current and potential customers across different phases of the buyer journey. They will conduct in\-depth analysis to understand the drivers of performance and identify and implement enhancements to optimize strategy and execution. This person will help drive the experimentation agenda, run quantitative analyses, and execute on growth levers identified through testing.
The candidate should thrive in a fast\-paced and dynamic environment, working cross\-functionally with Sales, Marketing Ops, Brand, and agency partners to drive results. They should have an entrepreneurial mind\-set, taking ownership in creating opportunities, aligning to organizational objectives, and being nimble in identifying and acting on new ways to drive pipeline.Key responsibilities and deliverables:* Co\-develop paid media strategy, bringing channel expertise and performance data to inform how and where we invest across paid search, paid social, programmatic display, and content syndication
- Own end\-to\-end campaign execution across all paid channels, driving MQL volume and hitting Cost Per Lead targets
- Co\-manage the paid media budget, track pacing, flagging reallocation opportunities, and making data\-backed recommendations on spend distribution across channels
- Develop audience segmentation and targeting strategies to reach advertising decision\-makers
- Lead structured A/B testing across creative, copy, audiences, and formats; translate results into channel strategy recommendations and budget decisions
- Manage content syndication vendor relationships end\-to\-end by vetting partners, ensuring lead quality, and tracking conversion through to pipeline
- Partner with Marketing Ops and other teams to maintain clean campaign tracking, UTM hygiene, Salesforce integration, and accurate attribution
- Collaborate with the Brand/Creative team on ad creative development and ensure messaging alignment across channels
- Work closely with Sales to align on target account lists, ICP criteria, and lead quality feedback loops
- Manage external agency relationships where applicable, serving as day\-to\-day lead on deliverables and performance
- Build and maintain performance dashboards and contribute to weekly and monthly demand gen reporting, surfacing insights that shape broader program strategy
Skills Needed:* 7\-10 years of hands\-on paid media experience with at least 4\+ years in B2B demand generation
- Demonstrated experience co\-owning or contributing to paid media strategy with a track record of results against MQL and CPL goals
- Deep expertise in LinkedIn Campaign Manager; strong command of its full ad product suite and a clear POV on what drives B2B performance
- Solid proficiency in Google Ads and Microsoft Advertising, including search campaign management, smart bidding, and audience targeting
- Hands\-on experience with programmatic display platforms (e.g., DV360, The Trade Desk, or equivalent DSPs)
- Experience managing content syndication programs with a focus on lead volume, quality, and ROI
- Comfort with shared budget ownership and able to track pacing, model scenarios, and make spend recommendations with confidence
- Working knowledge of Salesforce and marketing automation platforms (Marketo, HubSpot, or similar) including lead lifecycle and attribution
- Skilled in creating insightful measurement reports and dashboards that demonstrate the impact of paid media on pipeline and revenue
- Passion for staying up to date with the latest marketing technologies and trends, including AI\-assisted tools, and applying them to drive growth
- Self\-starter with an entrepreneurial mind\-set; thrives in lean, fast\-moving environments and brings proactive thinking to both strategy and execution
- Strong communicator who can engage credibly with senior stakeholders, Sales partners, and cross\-functional teams
Skills and Qualifications
Salary Range Pay Transparency: Compensation for this role, for candidates based in New York, NY is expected to be between Base range: $175,000 \~ $200,000\. 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.
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.
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.
Salary Context
This $175K-$200K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 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. This role's midpoint ($187K) sits 12% above the category median. Disclosed range: $175K to $200K.
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
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 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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