Temporary Machine Learning Trainer Jobs in Texas

Job Title: Machine Learning Developer

Location (city, state): Dallas, Texas - onstie 5x a week

Assignment Type: Direct Hire

Pay: $115,000–$140,000 annually, plus a short-term incentive and long-term incentive.

Benefits: This position is eligible for medical, dental, vision, and 401(k). The company offers fully paid family benefits, a generous 401(k) match, and a competitive paid-time-off program.

Our client is a well-established energy organization with significant operations in the Permian Basin. The company is expanding its artificial intelligence and machine learning capabilities and offers a collaborative environment where employees are trusted to take ownership, contribute ideas, and influence technical direction.

We are seeking a Machine Learning Developer to serve as the first dedicated ML engineering professional within a newly established AI/ML function. This individual will create the MLOps framework, development standards, and platform foundation needed to move machine learning models from experimentation into secure, reliable production environments.

This is a hands-on individual contributor role with significant influence over the organization’s future machine learning strategy. The successful candidate will be comfortable setting technical direction, recommending new approaches, and performing the detailed engineering work required to implement those recommendations. This opportunity is ideal for someone who enjoys building programs from the ground up and working in a fast-moving, entrepreneurial environment.

Key Responsibilities:

• Develop the organization’s MLOps strategy, technical standards, reusable workflows, and preferred process for moving models into production.
• Build and support machine learning solutions within the Databricks environment.
• Collaborate with data scientists to deploy models using tools such as MLflow, AutoML, Unity Catalog, and Databricks Model Serving.
• Create automated CI/CD processes for model training, deployment, testing, and promotion between environments.
• Manage the full model lifecycle, including experiment tracking, model registration, version control, lineage, governance, and user access.
• Implement monitoring and validation processes for production models, features, and source data.
• Establish operational visibility for ML systems and assist with troubleshooting and production support when issues arise.
• Develop standards for data quality, feature reliability, schema validation, and data version management.
• Produce technical documentation, reference designs, reusable templates, and engineering playbooks.
• Lead code reviews and share best practices with data science and engineering professionals.
• Work with business leaders, data scientists, data engineers, and IT teams to define requirements and encourage adoption of shared ML frameworks.
• Research emerging technologies and recommend enhancements to machine learning delivery, including GenAI, agent-based systems, and AI-assisted development tools.
• Present technical recommendations to stakeholders and confidently explain or defend a position when viewpoints differ.

Qualifications:

• Bachelor’s degree in computer science, data science, engineering, mathematics, statistics, or a related discipline is required.
• Three to five years of experience developing, deploying, or supporting machine learning or data-intensive production systems is preferred; candidates with more advanced experience are also encouraged to apply.
• Hands-on experience with Databricks MLflow and AutoML is required.
• Advanced Python skills with the ability to create clean, tested, and maintainable production code.
• Strong SQL capabilities and familiarity with Spark or another distributed data-processing technology.
• Experience implementing or supporting MLOps practices such as automated pipelines, model deployment, production monitoring, and lifecycle governance.
• Knowledge of software development fundamentals, including Git, unit testing, CI/CD, and common application design principles.
• Understanding of widely used machine learning algorithms, model-training methods, evaluation techniques, and hyperparameter tuning.
• Ability to translate complex technical topics for both technical and nontechnical audiences.
• Strong analytical, organizational, interpersonal, and problem-solving skills.
• Ability to work independently, manage competing priorities, and operate with limited supervision.