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

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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Showing results 1-20

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Pearland, TX

Machine Learning Engineer information

See Pearland, TX salary details

$28.1K

$114.8K

$172.5K

How much do machine learning engineer jobs pay per year?

As of Jun 28, 2026, the average yearly pay for machine learning engineer in Pearland, TX is $114,811.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,500.00 and $138,200.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

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-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies 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 they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

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 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 Pearland, TX? The most popular types of Machine Learning Engineer jobs in Pearland, TX are:
What are popular job titles related to Machine Learning Engineer jobs in Pearland, TX? For Machine Learning Engineer jobs in Pearland, TX, the most frequently searched job titles are:
What cities near Pearland, TX are hiring for Machine Learning Engineer jobs? Cities near Pearland, TX with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Pearland, TX as of June 2026, with employment types broken down into 1% As Needed, 97% Full Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $114,811 per year, or $55.2 per hour.

Senior Machine Learning Engineer - Agentic Workflow

Vitol

Houston, TX • On-site

$99K - $137K/yr

Full-time

Posted 22 days ago

Job description

Company Description

Vitol is a leader in energy and commodities. Vitol produces, manages and delivers energy and commodities to consumers and industry worldwide. In addition to its primary business of trading, Vitol is invested in infrastructure globally, with $10+billion invested in long-term assets.

Vitol's customers include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves its customers from some 40 offices worldwide. Revenues in 2024 were $331bn.

Our people are our business. Talent is precious to us and we create an environment in which individuals can reach their full potential, unhindered by hierarchy. Our team comprises more than 65+ nationalities and we are committed to developing and sustaining a diverse work force. Learn more about us here.

This Role is located in Houston, TX - In office 5x a week

Job Description

We are seeking a Senior Machine Learning Engineer / Platform Engineer to design and build a production-grade agentic workflow platform. This role sits at the intersection of LLM systems engineering, distributed platforms, and applied ML, with a strong emphasis on orchestration, reliability, and extensibility. You will be responsible for architecting and implementing agent-based workflows that integrate large language models, retrieval systems, structured knowledge, and external APIs-designed for robustness, observability, and real-world business use.

  • Design and implement multi-agent and single-agent workflows using orchestration patterns and tools, context engineering, memory management, and guardrail strategies.
  • Design RAG pipelines incorporating vector search, hybrid retrieval, and citation tracking.
  • Implement knowledge graph-backed reasoning, including ontologies, entity resolution and graph-based context construction.
  • Design evaluation frameworks for agent task completion correctness, quality, cost, and latency.
  • Develop and deploy machine learning models, focusing on production readiness, scalability, and performance.
  • Collaborate with data scientists to transition experimental models into robust, production-grade applications.
  • Integrate with collaboration platforms (e.g., Teams, alerting systems) for intelligent distribution of insights. 
  • Implement and manage CI/CD pipelines to automate deployment, testing, and monitoring of models.
  • Architect and deploy systems on AWS, leveraging compute, storage and security services
Qualifications
  • Bachelor's or master's degree in computer science, Engineering, or related field.
  • 6+ years of experience in software engineering, ML engineering, or platform engineering.
  • Strong proficiency in writing production-grade Python, and experience with Claude Code or Cursor.
  • Hands-on experience with LLM-based systems, including:
    • LangChain / LangGraph
    • MCP
    • Langsmith
    • Claude or comparable frontier models
    • AWS AgentCore or comparable agentic frameworks
  • Solid understanding of RAG architectures, embeddings, and vector search.
  • Experience designing and consuming APIs (REST and/or async/event-driven).
  • Strong cloud engineering experience on AWS.
  • Knowledge of how to fine-tune frontier models to specific domain knowledge
  • Experience with distillation, quantization and small language models is a plus
  • Experience deploying traditional machine learning models into production environments using MLOps tools and best practices.
  • Knowledge of distributed systems, large-scale model optimization, and API development.
  • Exceptional ability to work on a team - especially a dynamic, innovative "tiger team" developing early stage PoC systems.
  • Strong understanding of container orchestration and cloud-native application design.
  • Ability to work in dynamic environments, handling rapid experimentation and iterative development.
Additional Information

Personal Characteristics 

  • A self-motivated individual who thrives on seeing the results of their work and its impact on the business
  • Strong communication skills, both verbally and in writing
  • A keen sense for the art of the possible
  • Proven ability to be flexible and work hard, both independently and collaboratively
  • Methodical and organized - in general, in experimental design, and in code!
  • Attention to detail with strong analytical, mathematical, and problem-solving skills
  • An interest in learning about the energy commodities space
  • Resourceful and able to think creatively and adapt in a dynamic and energetic environment
  • Team player, with an open, non-political style and a high level of personal integrity
  • Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world's energy flows

This Role is located in Houston, TX - In office 5x a week

All your information will be kept confidential according to EEO guidelines.

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