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Samsung Machine Learning information

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$25.5K

$42.6K

$88K

How much do samsung machine learning jobs pay per year?

As of Jun 3, 2026, the average yearly pay for samsung machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer at Samsung, and why are they important?

To thrive as a Machine Learning Engineer at Samsung, you need a strong background in computer science, mathematics, and statistics, often supported by a relevant degree and experience with machine learning algorithms. Proficiency in programming languages such as Python or C++, familiarity with frameworks like TensorFlow or PyTorch, and knowledge of cloud platforms are typically required. Strong problem-solving, collaboration, and communication skills help you translate business needs into technical solutions and work effectively in cross-functional teams. These skills ensure the development of innovative, scalable AI solutions that drive Samsung's product advancements and competitiveness.

What are some common challenges faced by machine learning engineers at Samsung, and how does the company support overcoming them?

Machine learning engineers at Samsung often work on large-scale, cutting-edge projects that require handling vast datasets, optimizing model performance, and deploying solutions to millions of users. Common challenges include keeping pace with rapidly evolving technologies, ensuring data privacy, and collaborating across interdisciplinary teams. To support engineers, Samsung provides ongoing training, access to high-performance computing resources, and opportunities to collaborate with global experts. Additionally, the company fosters a culture of innovation, encouraging knowledge sharing and continuous learning.

What does a Samsung Machine Learning engineer do?

A Samsung Machine Learning engineer designs, develops, and implements machine learning algorithms and models to improve Samsung’s products and services. They work with large datasets, build predictive models, and collaborate with cross-functional teams to integrate AI capabilities into devices like smartphones, TVs, and home appliances. Their work helps enhance user experiences, optimize performance, and enable innovative features such as voice recognition, image processing, and smart automation.

What is the difference between Samsung Machine Learning vs Samsung Data Scientist?

AspectSamsung Machine LearningSamsung Data Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with ML frameworksDegree in Data Science, Statistics, or related; strong programming skills
Work EnvironmentDeveloping ML models, algorithms, and deploying AI solutionsAnalyzing data, building predictive models, interpreting results
Employer & Industry UsageTech companies, AI product teams, R&D divisionsBusiness units, analytics teams, product development

Samsung Machine Learning specialists focus on creating and deploying machine learning models, while Samsung Data Scientists analyze data to generate insights and support decision-making. Both roles require strong technical skills and often collaborate within the same industry sectors, but their core responsibilities differ in focus and application.

More about Samsung Machine Learning jobs
What states have the most Samsung Machine Learning jobs? States with the most job openings for Samsung Machine Learning jobs include:
What job categories do people searching Samsung Machine Learning jobs look for? The top searched job categories for Samsung Machine Learning jobs are:
Samsung Machine Learning jobs near you
Infographic showing various Samsung Machine Learning job openings in the United States as of May 2026, with employment types broken down into 38% Full Time, and 62% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Machine Learning Engineer

Machine Learning Engineer

Prospance Inc.

Mountain View, CA

Other

Posted 13 days ago

Job description

Job Title: Machine Learning Engineer 
Only on W2 
Duration: 12 months
Location: Mountain View, CA (Local candidates Required)
 
Position Summary
We are looking for an experienced Machine Learning Engineer to lead the development of prompt injection and prompt safety models that protect Client''''s downstream agentic AI systems across phone, cloud, and XR/AR. You will design, train, and deploy classifier and guardrail models (both cloud-based and hybrid on-device) that screen agent inputs and outputs for injection attacks, unsafe content, and policy violations. A core part of the role is post-training these models with RLHF, DPO, and related optimization techniques to push detection accuracy and false-positive rates beyond what off-the-shelf solutions provide.
 
Role and Responsibilities
  • Design and train prompt injection detection models and prompt safety classifiers that operate on both inputs to and outputs from Samsung''''s agentic AI systems.
  • Build hybrid deployment pipelines that split safety inference between on-device (phone, XR/AR) and cloud, optimizing for latency, privacy, and detection coverage.
  • Apply post-training techniques (e.g. RLHF, reward modeling, policy optimization) to optimize guardrail model performance, calibration, and robustness against adaptive adversaries.
  • Curate and generate adversarial training data: direct and indirect prompt injections, jailbreaks, tool-use exploits, and unsafe-output cases drawn from red-teaming and production signals.
  • Build evaluation harnesses that measure attack success rate, false-positive rate, latency, and on-device footprint across model iterations and threat categories.
  • Partner with agent, device, and platform teams to integrate safety models into mobile-use agents, XR/AR assistants, and cloud agentic workflows, and to close the loop from production incidents back into training data.
  • Work cross-functionally with security researchers, modeling teams, and product engineers; document methods and, where appropriate, contribute to patents and publications.
 
Required Qualifications
  • M.S. or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field; or B.S. with equivalent industry experience.
  • 3+ years of industry experience in ML engineering or applied AI research, with demonstrated ownership of production ML systems.
  • 2+ years of industry experience in software engineering.
  • Strong proficiency in Python and PyTorch (or JAX/TensorFlow), with solid software engineering fundamentals (version control, testing, and reproducible experimentation).
  • Hands-on experience post-training LLMs with RLHF, DPO, RLAIF, or reward modeling including reward design, preference data curation, and training stability.
  • Hands-on experience training and deploying classifier or guardrail models for safety, content moderation, abuse detection, or adversarial robustness.
  • Familiarity with prompt injection, jailbreak, and agentic AI threat models, and with distributed training frameworks (DeepSpeed, FSDP, Accelerate).
 
Preferred Qualifications:
  • Experience building safety or moderation systems for agentic AI: tool-use guardrails, indirect prompt injection defenses, or output filtering for autonomous agents.
  • Experience with red-teaming, adversarial data generation, or automated attack pipelines (e.g., GCG)

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