Machine Learning Engineer

$90K - $174K Taylor, TX, US Mid Level AI/ML Engineer

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Skills & Technologies

Drift AiPythonPytorchTensorflow

About This Role

AI job market dashboard showing open roles by category

About Samsung Austin Semiconductor

Samsung is a world leader in advanced semiconductor technology, founded on the belief that the pursuit of excellence creates a better world. At Samsung Austin Semiconductor, we are Innovating Today to Power the Devices of Tomorrow.

Come innovate with us!

Position Summary

As a Machine Learning Engineer at Samsung Austin Semiconductor, you will build and maintain the model pipelines for our anomaly detection and root cause analysis systems. You will work heavily with PySpark to process large\-scale time\-series and operational data, prepare training datasets, and manage the full model deployment lifecycle. Your day\-to\-day will involve designing robust ML pipelines, developing and validating models, and optimizing PySpark jobs for large\-scale processing. You will bridge the gap between model development and production, contributing to model tuning while taking ownership of the scalable systems that bring these models to life.

The team operates in a collaborative, sprint\-driven environment where you will have the autonomy to design technical approaches, test new tools, and iterate quickly based on feedback. Prior semiconductor experience is helpful but not required; you will learn the domain through hands\-on projects and direct support from the team.

Role and Responsibilities

Here’s What You’ll Be Responsible For:

  • Build PySpark workflows that ingest, clean, and transform high\-volume manufacturing data, converting raw signals into structured datasets ready for training and inference.
  • Optimize Spark jobs by tuning partition strategies, managing executor memory, minimizing shuffle operations, and handling skewed joins to reduce runtime and cluster resource usage.
  • Design and maintain end\-to\-end ML pipelines that automate feature calculation, model training, validation, and deployment, ensuring each run is reproducible and auditable.
  • Implement and tune machine learning models for anomaly detection and root cause analysis.
  • Manage the model lifecycle in production: track versions, store artifacts securely, trigger automated retraining, and execute rollback procedures when performance degrades.
  • Monitor pipeline execution times, data quality checks, and model metrics (accuracy, drift, throughput), building alerting rules to catch failures or degradation early.

Skills and Qualifications

Here's what you'll need:

Required

  • Bachelor’s degree or higher in Computer Science, Software Engineering, Data Science, or a related quantitative field.
  • 3–5\+ years of professional experience building and maintaining machine learning systems.
  • Strong proficiency in PySpark and distributed data processing, with experience optimizing jobs for speed and memory.
  • Hands\-on experience with Python ML libraries (scikit\-learn, TensorFlow, PyTorch, or XGBoost) for model training and evaluation.
  • Practical knowledge of MLOps practices, including pipeline orchestration, model versioning, experiment tracking, and deployment.
  • Experience setting up monitoring and alerting for both data pipelines and deployed models.

Preferred

  • Experience setting up model registries, automated retraining triggers, and rollback procedures to keep production models reliable.
  • Experience writing automated tests and validation checks for data pipelines and model outputs to catch errors before deployment.
  • Familiarity with on\-prem or private cloud infrastructure, including cluster management and secure artifact storage.

*The current base salary range for this role is between $90,000 \- $174,500\. Individual base pay rates will depend on factors including duties, work location, education, skills, qualifications and experience. Total compensation for this position will include a competitive benefits package and may include participation in company incentive compensation programs, which are based on factors to include organizational and individual performance.*

Total Rewards

At Samsung Austin Semiconductor, base pay is just one part of our total compensation package. The base compensation for this role will depend on education, experience, skills, and location.

We offer a comprehensive benefits package, including:

  • Medical, dental, and vision insurance
  • Life insurance and 401(k) matching with immediate vesting
  • Onsite café(s) and workout facilities
  • Paid maternity and paternity leave
  • Paid time off (PTO) \+ 2 personal holidays and 10 regular holidays
  • Wellness incentives and MORE

Eligible full\-time employees (salaried or hourly) may also receive MBO bonuses based on company, division, and individual performance.

All positions at Samsung Austin Semiconductor are full\-time on\-site.

U.S. Export Control Compliance

This role may require access to information subject to U.S. export control laws. Applicants must be authorized to access such information or eligible for government authorization.

Trade Secrets Notice

By submitting an application, you agree not to disclose to Samsung—or encourage Samsung to use—any confidential or proprietary information (including trade secrets) belonging to a current or former employer or other entity.

  • 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.
  • Samsung Electronics America, Inc. and its subsidiaries are committed to 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.

Salary Context

This $90K-$174K range is in the lower quartile 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

Title Machine Learning Engineer
Location Taylor, TX, US
Category AI/ML Engineer
Experience Mid Level
Salary $90K - $174K
Remote No

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 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

Drift Ai (2% of roles) Python (52% of roles) Pytorch (15% of roles) Tensorflow (12% of roles)

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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $90K to $174K.

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 Electronics AI Hiring

Samsung Electronics has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Taylor, TX, US, New York, NY, US, Plano, TX, US. Compensation range: $170K - $174K.

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

Based on 6,420 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $214,900. Actual compensation varies by seniority, location, and company stage.
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.
About 15% of the 4,317 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Samsung Electronics is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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