Systems Research Engineer (ML Infrastructure)

TL;DR

Systems Research Engineer (ML Infrastructure): Building and optimizing real-time ML inference systems for a global risk management platform with an accent on low-latency and fault-tolerance. Focus on designing cloud-native data and ML infrastructure to process millions of events per second.

Location: Portugal (Remote/Hybrid)

Company

hirify.global is a leading RiskOps platform for financial risk management, utilizing AI and machine learning to secure global commerce.

What you will do

• Design and implement real-time ML inference systems capable of processing millions of events per second.
• Define technical standards and best practices for cloud-native services in Data and ML Infrastructure.
• Drive architectural decisions for ML workloads, balancing performance, cost, and reliability.
• Execute the full software development life cycle, writing well-designed and testable code.
• Research state-of-the-art work in Data Processing and ML Systems, contributing to scientific papers and patents.
• Manage services throughout their lifecycle using DevOps principles.

Requirements

• BSc/MSc degree in Computer Science or a similar technical field.
• 4+ years of experience developing high-performance backend services.
• Strong understanding of distributed systems, multi-threading, and OO design principles.
• Proficiency in Java (or JVM languages) and Python.
• Ability to transform innovation research into clear product requirements.
• Experience with continuous delivery, monitoring, and operational ownership.

Nice to have

• Experience with Kubernetes and AWS cloud-native architectures.
• Familiarity with Big Data technologies like Spark, Kafka, Cassandra, EMR, or Dataflow.
• Fundamental knowledge of Machine Learning.
• Experience building AI-powered platforms for external customers.

Culture & Benefits

• Opportunity to work on disruptive, state-of-the-art research and contribute to open-source.
• Environment focused on ethical technology and long-term innovation.
• Comprehensive onboarding with training, connections, and managerial support.
• Flexible work options (virtually or onsite) depending on location.
• Inclusive workplace committed to equal opportunity and diversity.