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Machine Learning Engineer Python Jobs in Irving, TX

Vanguard

Manager, Machine Learning Engineer

Dallas, TX · On-site

$101K - $133K/yr

Leads a team of Machine Learning Engineers responsible for designing, building, deploying, and scaling AI/ML solutions that support Financial Advisory Services (FAS) business objectives. Partners ...

Cambium Learning Group

Software Engineer / Python

Dallas, TX · On-site +1

$46.25 - $63.75/hr

Train machine learning models, analyze performance metrics, and communicate results with visual and ... Extensive experience with Python and python frameworks (e.g. CherryPy, Bottle, Flask, Django)

Resolve Tech Solutions

Senior ML Engineer

Addison, TX

$101K - $138K/yr

Proficiency in programming languages such as Python, Java, or Scala. Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn. Strong understanding of ...

Cisco

Principal Machine Learning Engineer

Dallas, TX · On-site

$291K - $369K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/yr

... machine learning, data science, or a related field. • Proficiency in programming languages such as Python, Java, or Scala. • Experience with machine learning frameworks and libraries such as ...

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Machine Learning Engineer Python information

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$22.1K

$134.4K

$194.4K

How much do machine learning engineer python jobs pay per year?

As of Jul 10, 2026, the average yearly pay for machine learning engineer python in Irving, TX is $134,407.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,100.00 and $158,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Machine Learning Engineers working with Python, and how can they be addressed?

Machine Learning Engineers using Python often encounter challenges such as managing large datasets, ensuring efficient model deployment, and maintaining reproducibility of experiments. Handling data pipelines and model versioning can be complex, especially as projects scale. To address these issues, engineers typically use tools like Pandas and Dask for data handling, Docker for containerization, and MLflow or DVC for tracking experiments and models. Collaborating closely with data engineers, software developers, and product teams is also essential to streamline workflows and ensure models are production-ready.

What is the salary of machine learning engineer in Python?

The average salary for a machine learning engineer proficient in Python typically ranges from $90,000 to $150,000 annually, depending on experience, location, and industry. Senior roles or those requiring specialized skills in deep learning or data engineering may offer higher compensation.

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

To thrive as a Machine Learning Engineer Python, you need a solid background in computer science, statistics, and mathematics, along with proficiency in Python programming and machine learning concepts. Familiarity with frameworks such as TensorFlow, PyTorch, Scikit-learn, and experience with cloud platforms or MLOps tools are highly valued, as are certifications like Google Professional Machine Learning Engineer. Strong problem-solving abilities, communication skills, and a collaborative mindset help set you apart in this field. These skills enable engineers to design, implement, and deploy effective machine learning solutions that address real-world challenges in dynamic, team-oriented environments.

What is the difference between Machine Learning Engineer Python vs Data Scientist?

AspectMachine Learning Engineer PythonData Scientist
Required CredentialsBachelor's/Master's in CS, Data Science, or related; Python skills; ML certificationsBachelor's/Master's in Statistics, CS, or related; Python/R skills; Data analysis certifications
Work EnvironmentDevelops scalable ML models, deploys algorithms, collaborates with engineering teamsAnalyzes data, builds models, interprets results, communicates insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research institutions

While both roles require Python proficiency and data skills, Machine Learning Engineers focus on building and deploying scalable ML models, whereas Data Scientists analyze data and generate insights. The roles often overlap but differ in their primary focus and responsibilities.

What engineer makes $500,000 a year?

A senior or lead machine learning engineer with extensive experience, advanced skills in Python, deep learning, and data modeling can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Such roles often require advanced degrees, certifications, and a strong track record of successful projects.

What is a Machine Learning Engineer Python?

A Machine Learning Engineer Python is a professional who uses the Python programming language to design, build, and deploy machine learning models and systems. They work with large datasets, develop algorithms, and use Python libraries such as TensorFlow, scikit-learn, and PyTorch to solve complex problems. Their responsibilities also include preprocessing data, training models, evaluating performance, and integrating solutions into production environments. Machine Learning Engineers often collaborate with data scientists, software engineers, and business stakeholders to create scalable and efficient machine learning applications.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning engineer or AI director, often involving advanced skills in Python, deep learning, and data science. These roles usually require extensive experience, specialized knowledge, and may include leadership responsibilities or working in competitive industries like tech or finance.

Is Python enough for ML engineers?

Python is a fundamental programming language for machine learning engineers due to its extensive libraries like TensorFlow, PyTorch, and scikit-learn. However, proficiency in data manipulation, algorithms, and understanding of machine learning concepts, along with knowledge of tools like SQL and cloud platforms, are also important for success in the role.
What are popular job titles related to Machine Learning Engineer Python jobs in Irving, TX? For Machine Learning Engineer Python jobs in Irving, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Python jobs in Irving, TX look for? The top searched job categories for Machine Learning Engineer Python jobs in Irving, TX are:
What cities near Irving, TX are hiring for Machine Learning Engineer Python jobs? Cities near Irving, TX with the most Machine Learning Engineer Python job openings:
Machine Learning Engineer Python jobs near you
Sr. Principal Data Scientist / Machine Learning Engineer

Sr. Principal Data Scientist / Machine Learning Engineer

Ascentt

Plano, TX • On-site

Full-time

Re-posted 2 days ago

Job description

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring passionate builders to shape the future of industrial intelligence.
Job Summary
We're looking for an exceptionally skilled and experienced Sr. Principal Data Scientist / Machine Learning Engineer to lead and deliver high-impact AI/ML projects across Automotive domain. The ideal candidate will have a deep understanding of data science and machine learning tools, techniques, and algorithms, coupled with a proven track record of successfully leading projects from conception to deployment. This role demands strong client-facing communication skills and the ability to translate complex technical concepts into tangible business value.
Key Responsibilities
  • Technical Leadership & Strategy:
  • Serve as a primary technical expert and thought leader in Data Science and Machine Learning.
  • Define and drive the technical strategy for AI/ML initiatives, identifying high-value opportunities for optimization, predictive analytics, and process improvement across diverse use cases.
  • Architect and oversee the development of robust, scalable, and production-ready DS/ML models and solutions.
  • Stay at the forefront of the latest advancements in DS/ML, especially those applicable to various industries and large-scale data problems.
  • Project Leadership & Delivery:
  • Lead end-to-end DS/ML projects, including requirements gathering, data exploration, model development, validation, deployment, and monitoring.
  • Define project scope, timelines, and deliverables, ensuring successful execution within budget and schedule constraints.
  • Mentor and guide junior and mid-level data scientists and ML engineers, fostering a culture of technical excellence and continuous learning.
  • Drive MLOps best practices for reliable and efficient model deployment and lifecycle management.
  • Client Management & Communication:
  • Act as a trusted advisor to clients and internal stakeholders, understanding their business challenges and translating them into solvable DS/ML problems.
  • Effectively communicate complex analytical findings, model performance, and business recommendations to both technical and non-technical audiences.
  • Manage client expectations, present progress reports, and ensure stakeholder satisfaction.
  • Facilitate workshops and discovery sessions to identify new opportunities for AI/ML adoption.
  • Use Case Development & Problem Solving:
  • Lead the identification, prioritization, and execution of complex AI/ML use cases that drive significant business impact.
  • Apply deep analytical skills to dissect complex problems, derive actionable insights from data, and design innovative solutions.
  • Develop and implement models for:
  • Predictive Analytics: Forecasting, risk assessment, and anomaly detection.
  • Optimization: Improving efficiency, resource allocation, and decision-making.
  • Pattern Recognition: Identifying trends, segments, and relationships within large datasets.
  • Automation: Leveraging ML for intelligent process automation and enhanced operational efficiency.
  • Tool & Algorithm Proficiency:
  • Demonstrated expertise in a wide range of DS/ML tools and platforms (e.g., Python, R, TensorFlow, PyTorch, scikit-learn, Spark, AWS Sagemaker, Azure ML).
  • Deep understanding and practical application of various machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning, time series analysis, NLP, computer vision).
  • Proficiency in data manipulation, SQL, and working with large, complex datasets from various sources.

Qualifications
  • Master's or Ph.D. in Data Science, Machine Learning, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative field.
  • 8+ years of progressive experience in Data Science and Machine Learning roles, with at least 3-5 years in a leadership or principal-level capacity.
  • Demonstrated experience leading multiple end-to-end DS/ML projects successfully from concept to production.
  • Proven track record of managing client interactions, presenting technical solutions, and influencing strategic decisions.
  • Expertise in Python programming (NumPy, Pandas, Scikit-learn, Keras/TensorFlow/PyTorch).
  • Strong understanding of statistical modeling, experimental design, and hypothesis testing.
  • Experience with cloud platforms (AWS, Azure, GCP) and MLOps principles.
  • Excellent communication, interpersonal, and presentation skills.

Preferred Qualifications
  • Experience with real-time data processing and streaming analytics.
  • Knowledge of various industry verticals and their unique data challenges (e.g., finance, healthcare, retail, logistics, manufacturing).
  • Experience with large-scale data architectures (e.g., data lakes, data warehouses, distributed computing).
  • Publications or presentations in relevant fields.

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