Senior Machine Learning Engineer

We are seeking a Senior ML Engineer to join our Identification team. In this role, you will focus on designing, building, and maintaining production-grade ML solutions and infrastructure that power our fraud detection solutions.

You will collaborate closely with other team members to architect solutions that are reliable, scalable, and efficient. You will own features from concept to deployment and ensure seamless integration with other components in our platform.

Types of Projects and Impact:

• Take AI & ML applications from prototype to production, partnering closely with Data Scientists and cross-functional teams to ensure robust and performant deployment of machine learning solutions
• Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability
• Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions — from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining
• Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs
• Champion MLOps best practices: Establish & maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models
• Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams
• This role includes participation in a shared on-call rotation. The schedule will be communicated in advance, and we strive to balance coverage equitably while minimizing off-hours disruptions.

Required Skills:

• BS/MS in Computer Science, Data Science, or a related field, or equivalent work experience
• 6-10 years of experience as an ML Engineer
• Experience establishing and driving best practices for ML/MLOps in a growing technology organization
• Strong understanding of core ML concepts including supervised and unsupervised learning, model evaluation, and feature engineering
• Hands-on experience with modern ML frameworks such as CatBoost, LightGBM, TensorFlow, or PyTorch, and with large-scale data processing and transformation pipelines for training and serving models
• Experience deploying models to cloud platforms such as AWS, GCP, or Azure, using tools like SageMaker, Vertex AI, or Azure ML.
• Experience leveraging containerization and orchestration technologies such as Docker and Kubernetes
• Experience with CI/CD pipelines and MLOps tooling (e.g., MLflow, Feast, Weights & Biases).
• Ability to thrive in ambiguous environments where you get to work directly with stakeholders with minimal guidance and direction
• Proficient in English for clear communication in a global, remote team
• Role requires proficiency with macOS and comfort working fully remotely using tools such as Slack, Google Workspace, and Zoom.

Nice to Have:

• Experience working with GoLang or similar languages
• Experience working with Vector Databases such as Pinecone, Qdrant or similar technologies
• Practical experience with analytical storage systems like ClickHouse, Snowflake, BigQuery, Redshift, or Databricks.
• Experience with data transformation frameworks like dbt or other data pipeline tools.
• Familiarity with data visualization tools such as Apache Superset, Tableau, or Looker.

Technologies You Will Work With:

• Backend development: Python, GoLang
• ML frameworks: CatBoost, PyTorch
• Cloud platforms: AWS
• Data analytics/processing: ClickHouse, dbt, Apache Superset.

For US-based employees, the cash compensation range for this role is $204,000 - $235,000 . We set standard ranges for all US roles based on function, level, and geographic location, benchmarked against similar stage growth companies. To comply with local legislation and provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. However, these ranges are specific to the hiring location and may differ within or outside the US. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.