This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Engineer in the Americas.

As a Senior Machine Learning Engineer, you will play a critical role in building scalable and efficient ML solutions that support risk assessment and fraud detection for high-volume financial transactions. You’ll collaborate closely with Data Scientists, MLOps, and DataOps teams to implement production-grade models, optimize feature engineering, and monitor system performance. This position offers the opportunity to influence end-to-end machine learning workflows, contribute to real-time and batch processing architectures, and explore innovative approaches to emerging financial risks. You will thrive in a fast-paced, remote-first environment that values technical excellence, continuous learning, and cross-functional collaboration.

Accountabilities

Design, implement, and optimize machine learning models for production, supporting real-time and batch workflows.
Engineer domain-specific features to enhance model robustness and predictive accuracy.
Develop scalable pipelines for deploying, monitoring, and retraining ML models in production.
Build architectures optimized for latency, throughput, and resource efficiency, adhering to reliability and security standards.
Monitor model endpoints, pipeline performance, and key business metrics; implement recalibration and retraining as needed.
Collaborate with fraud analysts, risk managers, and product teams to translate business requirements into ML solutions.
Conduct research and prototypes to explore innovative ML approaches for emerging risk and fraud patterns.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related technical fields.
Strong experience in ML engineering and data science implementation.
Proficiency in Python, Scala, or Java.
Hands-on experience with batch and real-time streaming pipelines using SQL and NoSQL databases.
Familiarity with monitoring tools for pipelines, streaming systems, and model performance.
Experience with AWS cloud services (Sagemaker, EC2, EMR, ECS/EKS, RDS, etc.).
Knowledge of CI/CD pipelines, infrastructure-as-code tools (Terraform, CloudFormation), and MLOps platforms such as MLflow.
Expertise in machine learning modeling, especially tree-based and boosting algorithms for imbalanced targets.
Experience with online inference systems, APIs, and latency-sensitive services.
Strong proficiency in English.

Preferred/Plus:

ML applications in financial decision-making (credit risk, fraud prevention).
AWS Sagemaker or similar DS/ML workbench experience.
Containerization and orchestration (Docker, Kubernetes).
Feature store development and integration.
Distributed data systems (Kafka, Spark, Hadoop) and workflow orchestration tools (e.g., Airflow).

Benefits

Comprehensive health and dental coverage for you and your dependents, no co-payment.
Life insurance with extended coverage options.
Meal and supermarket vouchers, home office allowance, and team allowance.
Access to Wellhub for physical and online fitness activities.
Trustly Club discounts at partner institutions and stores.
English language online program with a private teacher.
Extended maternity and paternity leave, plus birthday off.
Flexible hours and fully remote work across Brazil.
Apple equipment (MacBook Pro, iPhone) and welcome kit.
Annual performance-based bonus.
Employee referral program with rewards.

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Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.
When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.

🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role.
Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!

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