Staff/Principal Machine Learning Engineer

Accountabilities: • Lead engineering initiatives that translate high-impact machine learning requirements into scalable, reusable infrastructure and tooling.

• Design and build platforms for training, serving, and managing machine learning representations, including unified embeddings capabilities.

• Streamline feature engineering workflows to reduce manual effort and accelerate the delivery of new signals.

• Develop automated continuous-learning systems covering data refresh, retraining, evaluation, and model drift monitoring.

• Scale training pipelines to support larger datasets, increasingly sophisticated architectures, and faster experimentation cycles.

• Improve the complete ML lifecycle, including data readiness, feature development, training, evaluation, serving, and production monitoring.

• Explore new algorithms and methodologies and develop the engineering capabilities needed to support them in production.

• Work backward from real-world modeling challenges to develop platform capabilities that improve model accuracy, efficiency, and scientific productivity.

• Define and influence the roadmap for next-generation ML platforms, balancing immediate business impact with long-term scalability.

• Collaborate with Data Engineering, ML Platform, Pricing, research, and other cross-functional teams to deliver reliable end-to-end machine learning systems.

• Provide technical leadership and influence engineering and scientific direction across teams and disciplines.

Requirements

• 5–7+ years of hands-on experience in applied machine learning, with substantial exposure to production-scale modeling.

• Strong theoretical and practical foundation in machine learning and statistics , including the ability to reason about model assumptions, bias, uncertainty, tradeoffs, evaluation, and failure modes.

• Deep understanding of how machine learning models work beyond the abstractions of common frameworks, with the ability to apply this knowledge to production systems.

• Demonstrated expertise across the end-to-end model development lifecycle, including data preparation, feature engineering, training, evaluation, and deployment.

• Experience working in high-scale, ML-driven product environments, particularly in fintech, pricing, risk modeling, or similarly complex domains.

• Strong proficiency in Python and core machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, and XGBoost.

• Ability to operate autonomously and provide technical direction in ambiguous, high-impact environments.

• Experience partnering with ML scientists, engineers, product teams, and other cross-functional stakeholders.

• Strong ability to bridge scientific and engineering disciplines and influence technical strategy across teams.

• Master’s degree or PhD in a quantitative discipline, or equivalent additional professional experience.

• Strong numerical reasoning, analytical ability, and comfort working at a fast pace.

• Practical experience with CUDA/GPU acceleration is preferred.

• Experience with feature store architecture, embedding systems, or synthetic data generation is a plus.

• Proven experience improving model accuracy in production with measurable business outcomes is preferred.

• Familiarity with modern experimentation frameworks, hyperparameter optimization, and automated model selection techniques is advantageous.

Benefits

• $220,700–$300,000 USD anticipated annual base salary , with actual compensation determined by geographic location, skills, experience, education, and training.

• Additional target bonus opportunities and annual equity grants that vest quarterly.

• 401(k) retirement benefits with a company match of $2 for every $1 contributed, up to $15,000 annually .

• Employee Stock Purchase Plan (ESPP) with discounted stock purchase opportunities for eligible U.S. employees.

• Comprehensive medical, dental, and vision coverage, along with wellness resources.

• Health Savings Account contributions for eligible plans.

• Life insurance and disability coverage.

• Paid time off, sick leave, and company holidays.

• Paid family and parental leave.

• Family-focused benefits supporting fertility, parenthood, and caregiving.

• Employee Assistance Program with mental health and life-support resources.

• Financial wellness resources, including financial planning tools and access to a financial concierge service.

• Annual wellness allowance supporting physical and emotional wellbeing and personal development.

• Annual productivity allowance for relevant tools and resources that support effective remote work.

• Connection and community through team events, company-wide updates, and employee resource groups.

• Digital-first remote working environment across the U.S., with opportunities for regular in-person collaboration.

• Most teams meet onsite approximately once or twice per quarter for 2–4 consecutive days, depending on team and role.

• Work aligned with East Coast or West Coast U.S. time zones .

• Opportunities to collaborate from offices in Burlingame, Columbus, Austin, or New York City when applicable.

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