Staff/Principal Machine Learning Engineer
New
J
JobgetherMachine learning
Based in United States, East Coast or West Coast U.S. time zonesFull-TimePrincipal
Salary$220,700–$300,000 USD anticipated annual base salary
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Job Details
- Experience
- 5–7+ years of hands-on experience in applied machine learning
- Required Skills
- PythonPyTorchTensorflowscikit-learn
Requirements
- Have 5–7+ years of hands-on experience in applied machine learning, with substantial exposure to production-scale modeling.
- Bring a strong theoretical and practical foundation in machine learning and statistics, including reasoning about model assumptions, bias, uncertainty, tradeoffs, evaluation, and failure modes.
- Understand how machine learning models work beyond common framework abstractions and apply that knowledge to production systems.
- Have experience across the end-to-end model development lifecycle, including data preparation, feature engineering, training, evaluation, and deployment.
- Have experience in high-scale, ML-driven product environments, particularly fintech, pricing, risk modeling, or similarly complex domains.
- Be proficient in Python and core machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, and XGBoost.
- Be able to operate autonomously and provide technical direction in ambiguous, high-impact environments.
- Have experience partnering with ML scientists, engineers, product teams, and other cross-functional stakeholders.
- Be able to bridge scientific and engineering disciplines and influence technical strategy across teams.
- Have a 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.
- Experience improving production model accuracy with measurable business outcomes is preferred.
- Familiarity with experimentation frameworks, hyperparameter optimization, and automated model selection techniques is advantageous.
Responsibilities
- Lead engineering initiatives that translate 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 delivery of new signals.
- Develop continuous-learning systems for data refresh, retraining, evaluation, and model drift monitoring.
- Scale training pipelines for larger datasets, more sophisticated architectures, and faster experimentation.
- Improve the machine learning lifecycle across data readiness, feature development, training, evaluation, serving, and production monitoring.
- Explore algorithms and methodologies and build the engineering capabilities needed to support them in production.
- Develop platform capabilities that address real-world modeling challenges and improve model accuracy, efficiency, and scientific productivity.
- Define and influence the roadmap for next-generation machine learning platforms.
- Collaborate with Data Engineering, ML Platform, Pricing, research, and other cross-functional teams to deliver end-to-end machine learning systems.
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