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Contract Machine Learning Software Engineer Jobs in Houston, TX

Software Engineer in Data Science

Houston, TX · On-site +1

$109K - $131K/yr

... Software Engineer to join our global data science and machine learning team. This role will have an ... The individual will work both with our data scientists and machine learning engineers but will also ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Machine Learning Tutor

Houston, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Data Engineer

Houston, TX

$109K - $131K/yr

... machine learning solutions. The ideal candidate can work independently, solve complex technical ... with Software Engineers, AI Engineers, Data Scientists, and business stakeholders to deliver ...

Our engineering culture is highly collaborative, and we emphasize daily pairing, continuous learning, and shared ownership. Contract Software Engineers at all levels are involved in writing clean ...

Showing results 41-60

Contract Machine Learning Software Engineer information

See Houston, TX salary details

$60.6K

$140.9K

$196.2K

How much do contract machine learning software engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for contract machine learning software engineer in Houston, TX is $140,881.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,600.00 and $165,200.00 per year, depending on experience, location, and employer.

What is a contract machine learning software engineer?

A Contract Machine Learning Software Engineer is a professional who is hired on a temporary or project basis to design, develop, and deploy machine learning models and systems. They often work with organizations that need specialized expertise for a limited duration, helping to build algorithms, analyze data, and integrate AI solutions into existing software products. Contract engineers typically have strong backgrounds in programming, mathematics, and data science, and they may work remotely or on-site. Their responsibilities can range from data preprocessing and model training to deploying models in production environments. This arrangement allows companies to access advanced machine learning skills without committing to a full-time hire.

What are the key skills and qualifications needed to thrive as a contract machine learning software engineer?

To thrive as a Contract Machine Learning Software Engineer, you need a strong background in computer science, proficiency in programming languages like Python, and expertise in machine learning algorithms, typically supported by a relevant degree or equivalent experience. Familiarity with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, along with knowledge of version control systems like Git, is essential. Strong problem-solving abilities, communication skills, and the ability to work independently or with cross-functional teams make someone stand out in this role. These skills ensure efficient delivery of scalable machine learning solutions that meet client requirements and project timelines.

How does a contract machine learning software engineer typically collaborate with full-time team members during a project?

As a Contract Machine Learning Software Engineer, you will often work closely with full-time data scientists, software engineers, and product managers. Collaboration usually happens through regular stand-up meetings, code reviews, and shared documentation platforms. Despite being a contractor, you’re expected to integrate seamlessly with the team, communicate progress transparently, and adapt to the company’s workflows. Building strong relationships and proactively seeking feedback can help ensure your contributions align with the project’s goals and timelines.

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

AspectContract Machine Learning Software EngineerData Scientist
CredentialsBachelor's or Master’s in CS, ML, or related fields; experience with ML frameworksBachelor's or Master’s in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentProject-based, often remote, focused on developing ML models and softwareData analysis, visualization, and interpretation, often in research or business settings
Employer & Industry UsageTech companies, startups, consulting firms; used for deploying ML solutionsResearch institutions, finance, healthcare, and tech; used for insights and decision-making

The main difference is that Contract Machine Learning Software Engineers focus on developing and deploying ML models as software solutions, while Data Scientists analyze data to generate insights. Both roles require strong technical skills, but their primary objectives and work environments differ.

What are the most commonly searched types of Machine Learning Software Engineer jobs in Houston, TX?

The most popular types of Machine Learning Software Engineer jobs in Houston, TX are:

What job categories do people searching Contract Machine Learning Software Engineer jobs in Houston, TX look for?

The top searched job categories for Contract Machine Learning Software Engineer jobs in Houston, TX are:

Infographic showing various Contract Machine Learning Software Engineer job openings in Houston, TX as of August 2026, with employment types broken down into 94% Full Time, and 6% Temporary. Highlights an 91% In-person, and 9% Remote job distribution, with an average salary of $140,881 per year, or $67.7 per hour.

Senior Machine Learning Engineer - Agentic AI

MD Anderson

Houston, TX • On-site

$146K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 23 days ago


Key responsibilities

  • Design, evolve, and operate the enterprise-scale agentic AI platform capabilities.

  • Build platform components that enable interoperability, validation, and operational safeguards for AI agents.

  • Define and document integration patterns, establish platform standards, and support incident response related to agentic AI systems.


MD Anderson Cancer Center rating

8.5

Company rating: 8.5 out of 10

Based on 172 frontline employees who took The Breakroom Quiz

12th of 898 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
Additional Information
  • Requisition ID: 178303
  • Employment Status: Full-Time
  • Employee Status: Regular
  • Work Week: Days
  • Minimum Salary: US Dollar (USD) 146,500
  • Midpoint Salary: US Dollar (USD) 183,000
  • Maximum Salary : US Dollar (USD) 219,500
  • FLSA: exempt and not eligible for overtime pay
  • Fund Type: Hard
  • Work Location: Remote (within Texas only)
  • Pivotal Position: Yes
  • Referral Bonus Available?: Yes
  • Relocation Assistance Available?: Yes

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