Principal Machine Learning Engineer at Jobgether – United States

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Principal Machine Learning Engineer in the United States.

This role provides an opportunity to shape and scale machine learning infrastructure that directly impacts predictive accuracy and operational efficiency across high-impact projects. As a Principal Machine Learning Engineer, you will design and implement systems that enable faster model development, automate workflows, and support large-scale production ML pipelines. You will collaborate closely with research scientists, data scientists, and platform engineers to translate modeling needs into scalable, reliable infrastructure. The position requires deep expertise in applied ML, strong programming skills, and a strategic mindset for solving complex technical problems. You will have autonomy to define technical direction while influencing cross-functional teams. This is a high-visibility role where your contributions will accelerate innovation and improve outcomes for millions of users. The environment is dynamic, collaborative, and digitally-first, offering flexibility and access to cutting-edge tools.

• Lead engineering initiatives to design, build, and scale ML infrastructure that accelerates model development and improves predictive accuracy
• Build unified embedding platforms for training, serving, and managing representations at scale
• Streamline feature engineering pipelines to reduce manual effort and enable rapid introduction of new signals
• Develop automated continuous-learning systems for data refresh, retraining, evaluation, and drift monitoring
• Scale training pipelines to handle larger datasets, more complex model architectures, and faster experimentation cycles
• Collaborate with ML scientists, platform engineers, and data teams to integrate systems that improve workflow efficiency and reliability
• Drive cross-functional technical discussions, influencing architecture and infrastructure decisions based on applied ML requirements
• Mentor and guide team members, promoting best practices in ML engineering, reproducibility, and operationalization

• 7+ years of hands-on experience in applied machine learning with production-scale modeling exposure
• Expertise in the full ML lifecycle: data preparation, feature engineering, training, evaluation, and deployment
• Experience in high-scale, ML-driven product environments, particularly in fintech, pricing, or risk modeling
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Scikit-learn, and XGBoost
• Strong cross-functional collaboration and ability to influence technical strategy across teams
• Advanced quantitative skills, capable of problem-solving in fast-paced, high-impact projects
• Master’s or PhD in a quantitative discipline, or equivalent professional experience
• Preferred: experience with GPU acceleration, feature stores, embedding architectures, synthetic data generation, hyperparameter tuning, and automated model selection

• Competitive base salary: $220,700 – $300,000 USD, with bonus opportunities and equity grants
• Generous 401(k) matching and Employee Stock Purchase Plan (ESPP)
• Comprehensive medical, dental, and vision coverage with company contributions
• Paid time off, sick leave, company holidays, and paid family/parental leave
• Health Savings Account contributions and income protection benefits
• Annual wellness and productivity allowances for personal development and tools
• Flexible digital-first work environment with opportunities for in-person collaboration at offices across the US
• Community engagement through team events, onsites, ERGs, and all-company updates