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Machine Learning Engineer Opt Jobs in Houston, TX

Pivotal Solutions

Senior AI Engineer

Houston, TX · On-site

$99K - $137K/yr

Design, develop, and deploy advanced AI and machine learning models to solve complex business ... Mentor junior engineers and provide technical guidance on AI best practices, model development, and ...

Varsity Tutors

Machine Learning Tutor

Sugar Land, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Missouri City, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Houston, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Pearland, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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All JobsMachine Learning Engineer Opt JobsMachine Learning Engineer Opt Jobs in Houston, TX

Machine Learning Engineer Opt information

See Houston, TX salary details

$30.1K

$123K

$184.8K

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

As of Jul 10, 2026, the average yearly pay for machine learning engineer opt in Houston, TX is $122,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,900.00 and $148,000.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

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 a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What are popular job titles related to Machine Learning Engineer Opt jobs in Houston, TX? For Machine Learning Engineer Opt jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Opt jobs in Houston, TX look for? The top searched job categories for Machine Learning Engineer Opt jobs in Houston, TX are:
What cities near Houston, TX are hiring for Machine Learning Engineer Opt jobs? Cities near Houston, TX with the most Machine Learning Engineer Opt job openings:
Machine Learning Engineer Opt jobs near you
Infographic showing various Machine Learning Engineer Opt job openings in Houston, TX as of July 2026, with employment types broken down into 72% Full Time, 22% Contract, and 6% Nights. Highlights an 100% In-person job distribution, with an average salary of $122,971 per year, or $59.1 per hour.
Senior Machine Learning Engineer - Agentic AI

Senior Machine Learning Engineer - Agentic AI

MD Anderson Center

Houston, TX • On-site

$99K - $137K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 24 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 168 frontline employees who took The Breakroom Quiz

29th of 880 rated healthcare providers

Job description

As a Senior Machine Learning Engineer - Agentic AI within Data Impact & Governance, you will be at the forefront of designing and operating the platform capabilities that enable autonomous and semi-autonomous AI systems to function reliably across clinical, research, and operational domains.
This role offers a rare opportunity to build enterprise-wide agentic AI platforms in a regulated healthcare environment-where correctness, safety, governance, and auditability matter as much as innovation and scale. You will influence technical standards, platform architecture, and operational safeguards that shape how agentic AI is adopted across one of the world's leading cancer centers.
What's in it for you?
  • Outstanding Benefits: MD Anderson offers paid medical benefits, generous paid time off (PTO), and strong retirement plans, providing stability and long-term financial security.
  • Enterprise-Level Impact: Architect platform capabilities that support AI agents operating across complex health IT systems and enterprise workflows.
  • Technical Leadership: Shape standards, integration patterns, and guardrails governing agentic AI at organizational scale.
  • Career Growth & Visibility: Partner closely with enterprise architects, applied MLEs, data scientists, IT, and governance leaders on high-impact AI initiatives.
  • Responsible AI Innovation: Work in a mission-driven institution where responsible AI, safety, and trust are central to technology strategy.
  • Collaborative Culture: Join a highly skilled team that values intellectual rigor, mentorship, and cross-disciplinary collaboration.

***The ideal candidate will have a healthcare background with at least 5 years of industry experience in data science and 3+ years as a Senior ML Engineer focused agentic AI systems***
Summary
The Senior Machine Learning Engineer - Agentic AI designs, evolves, and operates enterprise-scale agentic AI platform capabilities that enable safe, scalable, and governed deployment of autonomous and semi-autonomous AI systems. The role focuses on platform architecture, interoperability, validation frameworks, and operational safeguards that allow internal and third-party agent systems to function reliably in production healthcare environments.
This position operates at the intersection of autonomous AI behavior, enterprise systems integration, and regulated healthcare operations-where subtle failures can have systemic and high-impact consequences.
Major Work Activities
Core Responsibilities
  • Lead the design, evolution, and operation of the enterprise agentic AI platform in collaboration with enterprise architects and platform ML engineers.
  • Build platform components that enable interoperability between first-party and third-party agents, including identity, state, memory, tool access, orchestration, auditability, and policy enforcement.
  • Define and document standardized integration patterns connecting agents with enterprise business systems, data platforms, APIs, and health IT systems.
  • Provide reusable platform services, reference implementations, and SDKs that reduce risk and accelerate delivery for applied teams.
  • Design and operate validation and de-risking frameworks, including simulation, sandboxing, shadow execution, canary releases, and continuous behavior monitoring.
  • Establish and enforce platform standards for agent development, including interfaces, execution contracts, evaluation hooks, safety constraints, and observability requirements.
  • Participate in platform governance, release coordination, and incident response, supporting investigation and remediation of agent-related failures.
  • Implement platform safeguards such as fallback mechanisms, rollback strategies, approval gates, rate limiting, audit trails, and kill-switch capabilities.
  • Partner with software engineering, security, IT, and health IT stakeholders to deploy agentic AI capabilities in secure enterprise environments.
  • Support responsible AI practices through traceability of prompts, policies, tools, models, agent actions, and documentation of known failure modes and limitations.

Competencies
Technical Expertise
  • Experience building AI or ML platforms that serve multiple downstream teams and production workloads.
  • Strong proficiency in Python and integration of modern ML frameworks (e.g., PyTorch) with large language models and agent systems.
  • Hands-on experience with agentic AI frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or equivalent.
  • Working knowledge of agentic AI protocols and interoperability standards (e.g., MCP, agent-to-agent communication, structured tool invocation).
  • Experience implementing planner-executor loops, hierarchical agents, and multi-agent coordination patterns.
  • Familiarity with workflow orchestration tools (Airflow, Prefect, Temporal) and distributed execution frameworks (Ray or equivalent).
  • Experience deploying containerized AI platforms using Kubernetes in enterprise cloud environments with lineage, auditability, and controlled promotion to production.

Analytical Expertise
  • Ability to reason at the systems and platform level, balancing safety, performance, flexibility, and usability.
  • Experience designing quantitative evaluation strategies for agentic systems, including success rates, latency, cost, recovery behavior, and safety metrics.
  • Strong understanding of enterprise data governance, security, and privacy requirements, including healthcare and health IT considerations.
  • Ability to identify systemic risks stemming from agent autonomy, non-determinism, tool access, and multi-agent interactions.
  • Experience analyzing failure modes caused by prompt drift, model updates, tool changes, and cross-system dependencies.

Oral & Written Communication
  • Collaborate effectively with architects, applied MLEs, data scientists, software engineers, and IT partners.
  • Produce clear documentation covering platform architecture, APIs, integration patterns, validation frameworks, and operational runbooks.
  • Communicate platform capabilities, risks, and limitations to leadership and partner teams.
  • Contribute to internal standards and shared practices that improve safety, scalability, and consistency of agentic AI development.
  • Provide hands-on technical guidance, mentorship, and troubleshooting support to platform adopters.
  • Present technical and non-technical concepts clearly in meetings and institutional forums.

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 concentration 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 designing, deploying, and maintaining agentic AI systems that operate autonomously and collaboratively across distributed environments.
  • Experience in monitoring and troubleshooting autonomous agents post-deployment, 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 agentic AI platforms 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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