Sr. Staff Machine Learning Engineer
TLDR
Lead end-to-end production ML systems at scale in a fast-growing fintech, shaping product performance, risk intelligence, and customer experience through autonomous leadership.
- 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.
Requirements
Benefits
Benefits
Equity Compensation
Company equity enabling participation in long-term organizational growth.
Health Insurance
Comprehensive medical, dental, and vision insurance coverage.
Learning Budget
Access to modern ML infrastructure, cloud platforms, and continuous learning opportunities.
Paid Parental Leave
Parental leave and family-supportive policies.
Paid Time Off
Unlimited paid time off plus company holidays.
Remote-Friendly
Flexible remote-friendly work environment with occasional in-person onboarding and collaboration sessions.
Jobgether runs the largest remote job platform, effectively linking job seekers with over 200,000 flexible and remote opportunities that match their unique skills and preferences. Our focus is on enhancing the hiring process, ensuring efficiency while prioritizing the candidate experience, particularly in the growing health and wellness sector.
- Founded
- Founded 2020
- Employees
- 11-50 employees
- Industry
- Professional Services