About the company
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. You will operate at the intersection of applied machine learning and platform engineering, helping scale ML innovation across a high-impact technology environment.
Responsibilities
- 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
- Define and influence the roadmap for next-generation ML platforms
- Collaborate with Data Engineering, ML Platform, Pricing, research, and other cross-functional teams
- Provide technical leadership and influence engineering and scientific direction across teams
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
- Deep understanding of how machine learning models work beyond the abstractions of common frameworks
- Demonstrated expertise across the end-to-end model development lifecycle
- Experience working in high-scale, ML-driven product environments, particularly in fintech, pricing, risk modeling
- 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
- Master’s degree or PhD in a quantitative discipline, or equivalent additional professional experience
- Practical experience with CUDA/GPU acceleration is preferred
- Experience with feature store architecture, embedding systems, or synthetic data generation is a plus
- Familiarity with modern experimentation frameworks, hyperparameter optimization, and automated model selection techniques is advantageous
Conditions
- $220,700–$300,000 USD anticipated annual base salary
- 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
- Annual wellness allowance
- Annual productivity allowance
- Digital-first remote working environment across the U.S.