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Lab Summary:
Samsung Research America's Emerging Technologies Group identifies and incubates innovative external technologies that accelerate Samsung's development and market position of its industry\-leading consumer devices and medical equipment. We proactively research and create incubation, partnership \& investment opportunities with breakthrough technologies that lay the foundation for the future of Samsung's core products \& services across smartphones, tablets, wearables, PCs, TVs, appliances, and more. In addition to driving both the technical and business processes to validate and commercialize new technologies in Samsung's existing products, we maintain a strong pulse on the market to find the next big opportunity.
Position Summary:
At Samsung, we don't just build the next smartphone or wearable; we define the future of human\-device interaction. We are seeking a Senior Manager who sits at the unique intersection of deep hardware expertise, AI technology trends, and product strategy. You will not be managing a backlog of features; you will be an architect of what comes next.
In an era where AI is moving from the cloud to the edge (On\-Device AI), you will lead the ideation of groundbreaking consumer devices that leverage new sensor technologies, advanced AI models, and novel form factors. Your mission is to identify emerging hardware capabilities and translate them into viable, world\-class products that redefine the Samsung ecosystem. You are responsible for researching and developing collaborations with external innovations in various hardware subsystems (advanced computing, sensors, communications, etc.), and formulating strategies to pair them with machine learning capabilities to deliver innovative solutions through Samsung's vast product ecosystem. You will develop recommendations and actionable build\-buy\-partnership strategies to advance Samsung's product capabilities and provide competitive advantage.
In this role, you will perform technology research on the latest advances in novel computing, SoC, sensors, and other various hardware innovations from startups, universities, and regulatory institutions to create new internal collaboration projects with Samsung R\&D teams. With an aim towards advancing Samsung's product offerings across smartphones, wearables, TVs, XR devices, and more. you will develop business cases, promote the most relevant and impactful technology breakthroughs with our leadership team, foster partnerships with startups \& universities, and drive pivotal initiatives that increase Samsung's differentiation in the space.
Position Responsibilities:
- Analyze market trends, new breakthroughs in technology, and university research \& technology publications in hardware innovations that are relevant to Samsung's consumer electronics product offerings
- Evaluate emerging hardware technologies (e.g., neuromorphic sensors, low\-power AI accelerators, advanced MEMS, metamaterial antennas) and determine their product viability
- Synthesize global consumer trends with technical feasibility to propose next\-generation consumer devices, where AI is the core differentiator, before the market realizes they exist
- Bridge the gap between R\&D (chip design, sensor physics), Engineering (thermal, power, RF), and Business Strategy to turn abstract concepts into executable roadmaps
- Foster relationships with external technology startups, VCs, accelerators, and universities, to find breakthrough innovations and effectively communicate their value proposition to Samsung R\&D teams and executives globally
- Conduct rapid prototyping assessments, BOM modeling, and risk analysis to validate new product concepts before formulating development plans
- Be the primary liaison between partners and Samsung by leading discussions, facilitating evaluations, and driving POCs with an eye towards partnership, commercialization, investment, and/or M\&A
- Proactively address ambiguity through strategic thinking followed by tactical execution
Required Skills:
- Master's degree in Electrical Engineering, Computer Engineering, or related engineering discipline and/or PhD with a focus on hardware, sensors, or embedded systems or equivalent combination of education, training and experience
- 7\+ years of experience in consumer electronics hardware technologies as a technology researcher with a foundational understanding of novel semiconductor innovations and sensor technologies, with the last 2–3 years transitioning into Technical Product Management or Product Strategy
- Proven deep domain expertise in consumer device architecture, including SoC design, RF systems, power management, and sensor fusion
- Deep understanding of On\-Device AI, AI accelerator architectures, edge computing constraints, and how AI models interact with physical hardware (latency, power, thermal)
- Acumen to convey findings succinctly in layman's terms as well as envision and explain hypothetical technical implementation in and impacts on existing hardware technology stacks
- Effective interpersonal and relationship management skills, especially influencing stakeholders without direct authority
- Experience in leading complex, multi\-team and cross\-geographic projects to success
- Excellent written, verbal communications and presentation skills
- Ability to deal with ambiguity associated with working in a fast\-paced and changing environment
- Strong personal network within the industry, startup and ventures community
- Must be willing to travel domestically and internationally (15%)
Special Attributes:
- Great communication skills: clear, patient, and well adapted to the audience and situation
- Analytical skills to formulate and present structured, objective recommendations and respond to challenges and objections
- Personal skills to plan, organize, and prioritize multiple assignments and delivery on tight schedules
- Ability to position and sell technology to executive audiences
Additional Information
Disclosure of Trade Secrets
Samsung has a strict policy on trade secrets. In applying to Samsung and progressing through the recruitment process, you must not disclose any trade secrets of a current or previous employer.
Essential Job Functions
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, and frequently operate standard office equipment, such as telephones and computers.
Samsung Research America is committed to complying with all Federal, State and local laws related to the employment of qualified individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact the recruiter or email [email protected].
Equal Employment Opportunity
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 Research America is 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.
For more information regarding protection from discrimination under Federal law for applicants and employees, please refer to this link: Pay Transparency
Salary Context
This $198K-$272K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Samsung Research America, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($235K) sits 9% above the category median. Disclosed range: $198K to $272K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Samsung Research America AI Hiring
Samsung Research America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $272K - $272K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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