Machine Learning Engineer

• Deploy and productionize machine learning models developed by data science teams
• Build and maintain model deployment pipelines, including packaging, testing, versioning, release management, and rollback processes
• Optimize models and inference services for latency, throughput, scalability, reliability, cost, and resource efficiency
• Support batch, streaming, real-time, and API-based model serving environments
• Partner with data scientists to understand model logic, features, dependencies, validation metrics, and expected production behavior
• Translate model artifacts and technical specifications into production-ready code and services
• Implement monitoring for model performance, data quality, feature drift, model drift, latency, availability, and prediction quality
• Support model validation, A/B testing, champion/challenger testing, and controlled rollout strategies
• Contribute to MLOps capabilities such as CI/CD, model registries, feature stores, orchestration, observability, and automated testing
• Troubleshoot production issues related to model serving, data pipelines, infrastructure, and performance
• Ensure production ML solutions meet requirements for security, compliance, explainability, auditability, and operational resilience
• Work with data scientists, engineers, product teams, and platform teams to move models from experimentation into scalable, reliable, and secure production systems
• Travel 5-10% of the time

Senior ML Engineer – Consultant

• Develop, test, implement, and integrate processes with business applications using machine learning model predictions and classifications
• Collaborate with Data Scientists to refactor model code into IT-maintainable solutions following coding standards and best practices
• Apply ML development standards and coding best practices
• Contribute to machine learning projects across the lifecycle, including analysis, solution design, data pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration
• Support GenAI applications, ML frameworks/libraries, and ML models
• Design and write test scripts to verify data integrity and functionality
• Review existing test scripts
• Develop familiarity with machine learning engineering best practices through training, documentation review, and code review
• Configure, manage, and set up AI/ML infrastructure components in cloud and on-premises environments, including AWS, GCP, and graph databases
• Identify, measure, monitor, and control risks in accordance with risk and compliance policies and procedures

AI & Machine Learning Engineer

• Design and deploy surrogate and reduced-order models that replace or accelerate high-fidelity multiphysics simulations
• Develop physics-informed machine learning and physics-informed neural networks embedding physical constraints into model architectures
• Build and maintain ML pipelines for training, validation, uncertainty quantification, and continuous refinement against experimental and simulation data
• Implement active learning and Bayesian optimization workflows to guide design-space exploration and reduce costly simulation runs
• Integrate trained ML models into the Digital Twin framework, interfacing with HPC simulation outputs and real-time sensor data
• Develop anomaly detection and predictive diagnostics models for laser subsystem health monitoring
• Apply reinforcement learning and Bayesian control approaches to optimize laser operating parameters
• Collaborate with Digital Twin architects, systems engineers, optical simulation scientists, and other interdisciplinary teams
• Establish best practices for model versioning, reproducibility, testing, and documentation
• Support faster design cycles, predictive system optimization, and real-time decision support across laser, targetry, and fusion programs

• Own machine learning projects end to end, from understanding the business need and defining the approach through execution, launch, measurement, and iteration
• Develop and improve the Marketplace Health Model system to identify behaviors associated with trust, safety, and marketplace-health risks
• Translate complex marketplace challenges into scalable machine learning and statistical modeling solutions
• Partner with product, engineering, operations, trust and safety, and other stakeholders to understand needs and deliver practical solutions
• Identify opportunities where machine learning can create meaningful, measurable business impact
• Evaluate opportunities based on potential return on investment, customer impact, risk reduction, and implementation feasibility
• Design experiments and analyses to assess model performance, business outcomes, and marketplace-health improvements
• Monitor deployed models and recommend enhancements to maintain quality, reliability, and relevance as marketplace behavior evolves
• Communicate technical findings, recommendations, trade-offs, and project progress to technical and non-technical audiences
• Identify gaps, resolve blockers, and advance projects independently while keeping stakeholders aligned

Machine Learning Engineer jobs in Texas

Senior ML Engineer – Consultant

AI & Machine Learning Engineer

Machine Learning Engineer

Machine Learning Scientist II