Staff Machine Learning Engineer

Summary

Join Trustly's Data Science team as a Staff Machine Learning Engineer and play a pivotal role in driving the model development and production lifecycle. Collaborate with Data Scientists, MLOps, and DataOps teams to implement ML models for assessing transactional risk and fraud. Build pipelines to deploy machine learning models in production, focusing on scalability and efficiency. Implement systems to monitor model performance and create model-retraining pipelines. Design and implement scalable architectures to support real-time/batch solutions. Conduct research and prototypes to explore novel approaches in ML engineering. Mentor junior MLEs and contribute to internal learning initiatives. Partner with cross-functional teams to translate business requirements into ML solutions. This role is essential for ensuring efficient, reliable, and scalable workflows to power data-driven insights and machine learning solutions.

Requirements

• Bachelor’s or Master’s degree in CS/Engineering/Data-Science or other technical disciplines
• Substantial years of experience in DS/ML engineering
• Proficiency in programming languages such as Python, Scala, or Java
• Hands-on experience in implementing batch and real-time streaming pipelines, using SQL and NoSQL database solutions
• Hands-on experience in implementing monitoring for data pipelines, streaming systems, and model performance
• Experience in AWS cloud services (Sagemaker, EC2, EMR, ECS/EKS, RDS etc.)
• Experience with CI/CD pipelines, infrastructure-as-code tools (e.g., Terraform, CloudFormation), and MLOps platforms like MLflow
• Experience with Machine Learning modeling, notably tree-based and boosting models supervised learning for imbalanced target scenarios
• Experience in implementing online Inference systems, APIs, and services that respond under tight time constraints
• Proficiency in containerization and orchestration tools such as Docker and Kubernetes
• Proficiency in English

Responsibilities

• Model Development and Optimization: Design the data-architecture flow for the efficient implementation of real-time model endpoints and/or batch solutions
• Data Exploration and Feature Engineering: Engineer domain-specific features that can enhance model performance and robustness
• Productionization of ML Models: Build pipelines to deploy machine learning models in production with a focus on scalability and efficiency; Design and conduct model experimentation to test/improve the model’s performance; Implement, enforce, and iteratively improve the release management process for models and rules
• Monitoring, Maintenance & Improvement: Implement systems to monitor model performance, endpoints/feature health, and other business metrics; Create model-retraining pipelines to boost performance, based on monitoring metrics; Model recalibration
• Scalable System Design: Design and implement scalable architectures to support real-time/batch solutions; Optimize algorithms and workflows for latency, throughput, and resource efficiency; Ensure systems adhere to company standards for reliability and security
• Innovation and Continuous Improvement: Conduct research and prototypes to explore novel approaches in ML engineering for addressing emerging risk/fraud patterns
• Collaborative Problem Solving: Act as a key contributor to the team’s technical decision-making processes. Mentor junior MLEs and train them on routine tasks. Contribute to internal learning initiatives, such as code reviews & technical presentations. Partner with fraud analysts, risk managers, and product teams to translate business requirements into ML solutions

Preferred Qualifications

• Prior experience with ML applied to financial decision-making, such as credit risk, fraud prevention
• Prior experience with AWS Sagemaker and/or similar DS/ML workbench
• Feature store development and integration experience
• Experience with distributed data systems such as Kafka, Spark, Hadoop, and workflow/data orchestration tools (e.g., Airflow)

Benefits

• Bradesco health and dental plan, for you and your dependents, with no co-payment cost
• Life insurance with differentiated coverage
• Meal voucher and supermarket voucher
• Home Office Allowance
• Wellhub - Platform that gives access to spaces for physical activities and online classes
• Trustly Club - Discount at educational institutions and partner stores
• Monthly happy hours with iFood coupon
• English Program - Online group classes with a private teacher
• Extended maternity and paternity leave
• Birthday Off
• Flexible hours/Home Office - our culture is remote-first! You can work in every city in Brazil
• Welcome Kit - We work with Apple equipment (Macbook Pro, iPhone) and we send many more treats! Spoiler alert: Equipment can be purchased by you according to internal criteria!
• Annual premium - As a member of our team, you are eligible to receive an annual bonus, at the company's discretion, based on the achievement of our KPIs and individual performance
• Referral Program - If you refer a candidate and we hire the person, you will receive a reward for that!