Sr. Staff Machine Learning Engineer
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Sr. Staff Machine Learning Engineer in United States.
This senior-level role sits at the core of a fast-scaling fintech environment, where machine learning directly shapes product performance, risk intelligence, and customer experience. You will lead the end-to-end design and deployment of production-grade ML systems that operate at scale and deliver measurable business impact. The role blends deep technical ownership with cross-functional collaboration across data science, product, and engineering teams. You will work on high-impact problems such as prediction modeling, optimization, and large-scale data processing in a cloud-native environment. With significant autonomy, you will define best practices for ML system design, monitoring, and lifecycle management. This is a hands-on leadership role ideal for someone who thrives in building robust, scalable systems in dynamic, data-rich environments.
Accountabilities:
- 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.
- 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.
- Competitive compensation package aligned with market benchmarks and experience level.
- Company equity enabling participation in long-term organizational growth.
- Comprehensive medical, dental, and vision insurance coverage.
- 401(k) retirement savings plan with employer match (for U.S.-based employees).
- Unlimited paid time off plus company holidays.
- Parental leave and family-supportive policies.
- Flexible remote-friendly work environment with occasional in-person onboarding and collaboration sessions.
- Access to modern ML infrastructure, cloud platforms, and continuous learning opportunities.