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Machine Learning Engineer Jobs in Conroe, TX (NOW HIRING)

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Machine Learning Engineer information

See Conroe, TX salary details

$27K

$110.2K

$165.7K

How much do machine learning engineer jobs pay per year?

As of Jul 15, 2026, the average yearly pay for machine learning engineer in Conroe, TX is $110,243.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,900.00 and $132,700.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Conroe, TX? The most popular types of Machine Learning Engineer jobs in Conroe, TX are:
What job categories do people searching Machine Learning Engineer jobs in Conroe, TX look for? The top searched job categories for Machine Learning Engineer jobs in Conroe, TX are:
What cities near Conroe, TX are hiring for Machine Learning Engineer jobs? Cities near Conroe, TX with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Conroe, TX as of July 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $110,243 per year, or $53 per hour.
Senior Machine Learning Engineer - Medical Imaging

Senior Machine Learning Engineer - Medical Imaging

MD Anderson Center

Houston, TX • On-site

$99K - $137K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 13 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 169 frontline employees who took The Breakroom Quiz

27th of 885 rated healthcare providers

Job description

As a Senior Machine Learning Engineer specializing in medical imaging within the Data Impact & Governance department, you will help shape the future of clinical AI by building, deploying, and operating imaging models that directly impact patient care. This role offers the unique opportunity to work at the cutting edge of applied medical imaging ML within a world-renowned cancer center-where your solutions influence diagnosis, treatment, safety, and operational excellence.
What's in it for you?
  • Exceptional Benefits: MD Anderson provides paid medical benefits, generous PTO, and strong retirement plans, supporting your health, well-being, and long-term financial security.
  • High-Impact Work: Your models will be used in real clinical workflows-helping clinicians detect disease, streamline operations, and support better outcomes for patients.
  • Advanced Technical Environment: Work with large-scale imaging datasets, enterprise GPU infrastructure, distributed compute, and cutting-edge ML technologies-all within a governed clinical environment.
  • Career Growth & Visibility: Collaborate closely with clinicians, data scientists, ML leadership, radiologists, and operational teams. Your work will influence institutional AI strategy and governance.
  • Innovation with Responsibility: Help advance safe, ethical, and trustworthy AI practices in one of the world's leading cancer centers.
  • Collaborative Culture: Be part of a mission-driven organization that values innovation, learning, and teamwork.

Summary
The Senior Machine Learning Engineer - Medical Imaging owns the full lifecycle of clinical computer vision models deployed across the enterprise. This includes defining clinical ML problems, designing and training models, conducting rigorous validation, deploying models into clinical environments, and ensuring ongoing performance and reliability in real-world workflows.
The role is intended for engineers experienced in deploying and operating medical imaging ML models in production-especially within regulated, clinical, or safety-sensitive settings. You will collaborate with multidisciplinary teams, investigate model performance issues such as distribution shift or protocol variability, and ensure responsible AI adoption through strong documentation, traceability, and governance alignment.
Major Work Activities
Core Responsibilities
  • Own the full lifecycle of medical imaging ML models-from problem definition and model development to deployment, monitoring, maintenance, and retirement.
  • Participate as a technical owner in formal governance, release, and incident review processes, with clear escalation paths and responsibilities.
  • Translate clinical imaging use cases into deployable AI solutions with defined evaluation metrics, operating thresholds, and reproducible implementation strategies.
  • Design and execute post-deployment monitoring, including detection and mitigation of model degradation due to distribution shift, scanner changes, or labeling variability.
  • Collaborate with ML platform, data science, IT, and clinical operations teams to deploy and operate models in secure enterprise environments.
  • Maintain responsible AI practices, ensuring traceability of data, models, experiments, and documentation of limitations and failure modes.
  • Contribute to fallback, rollback, and model decommissioning strategies to support patient safety and operational continuity.
  • Engage clinical, technical, and operational partners to support safe adoption and communicate model risks, behaviors, and performance.
  • Mentor junior team members and contribute to best practices, review standards, and reproducible ML workflows.

Competencies
Technical Expertise
  • Experience developing, deploying, and operating medical imaging ML models in regulated clinical environments.
  • Ability to build imaging data pipelines involving DICOM workflows, dataset versioning, and distributed training.
  • Deep proficiency in Python and PyTorch for model training and inference under GPU and memory constraints.
  • Experience orchestrating ML workflows using Airflow, Prefect, or similar DAG-based systems.
  • Skilled in deploying containerized ML workloads on enterprise cloud platforms such as Azure using Kubernetes.
  • Understanding of audit-ready model tracking, lineage, and controlled promotion workflows.

Analytical Expertise
  • Ability to scope medical imaging ML projects end to end, considering clinical and regulatory constraints.
  • Experience designing validation strategies aligned with governance, regulatory expectations, and change control processes.
  • Knowledge of healthcare data privacy requirements as they relate to medical imaging and clinical metadata.
  • Ability to evaluate model performance quantitatively in the context of clinical workflows and operational realities.
  • Experience engaging clinicians, patient safety, and business stakeholders to communicate model performance, impacts, and risk considerations.
  • Ability to assess model generalizability and failure modes across scanners, sites, and populations.

Oral & Written Communication
  • Collaborate effectively with data scientists, ML engineers, software teams, clinicians, and operational leaders to integrate imaging models into real workflows.
  • Produce clear, comprehensive technical documentation including design specs, validation reports, and runbooks.
  • Communicate project risks, timelines, and outcomes to leadership and governance bodies.
  • Contribute to internal technical standards, best practices, and shared ML development frameworks.
  • Present technical and non-technical updates clearly across multiple stakeholder groups.

Education Required: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
Preferred Education: Master's Degree or PHD with a concertation in Science, Engineering, or related field.
Experience Required: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years' experience required. With PhD, one year of experience required.
Preferred Experience:
  • Experience operating medical imaging ML systems across multiple sites, scanners, or protocols, rather than a single controlled environment.
  • Experience handling post-deployment failures, including performance degradation, clinical incidents, model updates, or corrective actions.
  • Experience raising the technical bar for team members, such as establishing reproducibility practices, review standards, or shared patterns.
  • Experience technically evaluating third-party medical imaging AI within clinical workflows.
    The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
    This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
    It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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