Sr. Staff Machine Learning Engineer
United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonCloud ComputingKubernetesPyTorchSparkTensorflowscikit-learnNLPMLOps
Requirements
- Master’s or Ph.D. in Computer Science, Engineering, or a related technical field.
- 6+ years of experience building and deploying machine learning systems in production environments.
- Strong programming skills in Python and hands-on experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Proven experience designing end-to-end ML pipelines, including data processing, model training, deployment, and monitoring.
- Strong background in cloud platforms such as Amazon Web Services, Google Cloud, or Microsoft Azure.
- Experience with distributed computing frameworks like Apache Spark and orchestration tools such as Kubernetes.
- Strong knowledge of model optimization, CI/CD practices, version control, and MLOps principles.
- Excellent analytical thinking, problem-solving skills, and ability to collaborate across cross-functional teams.
- Exposure to NLP models and/or fraud, risk, or fintech-related ML domains is a strong plus.
Responsibilities
- Own the end-to-end lifecycle of machine learning systems, including data ingestion, preprocessing, model training, deployment, monitoring, and ongoing optimization in production environments.
- Design and maintain scalable and reliable data pipelines supporting large-scale model training and inference workflows.
- Develop, implement, and optimize machine learning models that meet performance, latency, scalability, and business requirements.
- Partner with data scientists, product managers, and engineers to translate product goals into production-ready ML solutions.
- Continuously evaluate and improve model performance through experimentation, hyperparameter tuning, and monitoring of real-world outcomes.
- Leverage distributed systems and cloud infrastructure to process and analyze large-scale datasets efficiently.
- Stay current with advancements in machine learning, MLOps, and production engineering practices to continuously enhance system capabilities.
- Apply domain expertise (e.g., payments risk, fraud detection, or customer success) and NLP approaches where relevant to improve product intelligence.
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