Senior Machine Learning Engineer

About the role

TalentReach is hiring on behalf of a rapidly growing technology company seeking an experienced Senior Machine Learning Engineer to develop and deploy production-scale machine learning solutions that power intelligent decision-making across a high-volume global platform. You will own the full machine learning lifecycle, from problem definition and feature engineering through model training, deployment, monitoring, and continuous optimization.

Responsibilities

• Design, develop, and deploy production machine learning models that solve recommendation, prediction, ranking, and forecasting problems
• Partner with Product Managers and Engineering teams to translate business challenges into scalable machine learning solutions
• Build and maintain data pipelines, feature engineering workflows, and model training infrastructure
• Deploy, monitor, and continuously improve machine learning models in production environments
• Evaluate model performance using offline experimentation and production metrics while monitoring for model drift
• Develop scalable infrastructure supporting model serving, orchestration, and continuous deployment
• Research emerging machine learning techniques and prototype new modeling approaches
• Conduct experiments to validate model improvements and measure business impact
• Write high-quality, maintainable, and well-tested production code
• Collaborate with cross-functional engineering teams to improve machine learning infrastructure and development practices

Requirements

• Master's or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Artificial Intelligence, or a related quantitative discipline, or equivalent professional experience
• 3+ years of experience building, deploying, and supporting production-grade machine learning systems
• Strong software engineering skills with experience developing production applications
• Experience training, tuning, deploying, monitoring, and maintaining machine learning models at scale
• Strong understanding of modern machine learning techniques, experimentation, and model evaluation
• Experience building feature engineering pipelines and scalable ML workflows
• Experience with TensorFlow, PyTorch, or similar machine learning frameworks
• Strong analytical, problem-solving, and communication skills
• Ability to work in a hybrid environment with four days per week onsite

Preferred Qualifications

• Experience with recommendation systems, ranking algorithms, click-through rate prediction, forecasting, or conversion modeling
• Knowledge of Bayesian methods, multi-task learning, meta-learning, or related machine learning approaches
• Experience with Kubeflow, feature stores, MLOps platforms, or machine learning orchestration frameworks
• Familiarity with production model serving and monitoring infrastructure
• Experience with modern recommendation model architectures or deep learning frameworks
• Experience designing and evaluating large-scale online and offline machine learning experiments

Pay

Target Total Compensation: $335,000 - $400,000
Base Salary: $210,000 - $260,000
Equity opportunity

Benefits

• 401(k) with company matching
• Comprehensive medical, dental, and vision coverage
• Wellness, technology, mobile, and commuter benefits
• Catered meals and stocked office
• Generous paid time off and additional leave programs

Schedule

Hybrid role based in Austin, TX with 4 days onsite per week.