Staff/Principal Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff/Principal Machine Learning Engineer based in United States. As a Staff/Principal Machine Learning Engineer, you will operate at the intersection of applied machine learning and platform engineering, helping scale ML innovation across a high-impact technology environment. You will design foundational tools, infrastructure, and workflows that accelerate model development and improve predictive performance. The role spans the full machine learning lifecycle, from data preparation and feature engineering through training, evaluation, deployment, and monitoring. You will work closely with research scientists, data scientists, ML engineers, and product teams to turn complex modeling needs into scalable solutions. Your work will help automate repetitive processes, improve experimentation speed, and enable scientists to focus on high-value modeling and analysis. This is a high-influence position for an experienced ML engineer who enjoys technical leadership, cross-functional collaboration, and solving ambiguous problems at scale.

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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