Systems Research Engineer- Software Engineer, Data and ML Infrastructure

Remote from: Portugal
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 9 May 2026
Apply before: 9 Jun 2026
Experience level: Senior
Views / Applies: 350 / 125

The world’s first end-to-end financial crime prevention platform protecting people and payments with AI-native solutions.

Computer Software
2008

AI Summary

Feedzai is seeking a Systems Research Engineer to join its Data and ML Infrastructure team, focusing on building real-time ML inference systems that process millions of events per second with low latency and fault tolerance. The role involves designing cloud-native services, driving architectural decisions, and contributing to scientific research and patents. Candidates should have a BSc/MSc in Computer Science, 4+ years of backend development experience, and strong Java/Python skills. Preferred qualifications include experience with Kubernetes, AWS, and big data technologies like Spark and Kafka. This is a remote opportunity to work on cutting-edge fraud prevention technology.

Job Complexity

Easy Hard

AI Insight This role demands a rare combination of deep technical expertise in distributed systems, ML infrastructure, and research skills, along with the ability to produce scientific publications. The seniority and breadth of requirements make it challenging, but not the highest difficulty due to clear qualifications.

Salary Analysis

AI Insight The salary range for this role is not provided, but based on market data for a senior systems research engineer in ML infrastructure, a competitive median salary would be around $160,000. The offered salary is expected to be competitive, reflecting the specialized skills and experience required.

Key Skills

Java Python Distributed Systems Machine Learning Kubernetes AWS Spark Kafka Cloud-Native Real-Time Systems

Cover Letter Sample

Dear Hiring Team,

I am writing to express my strong interest in the Systems Research Engineer position at Feedzai. With a background in computer science and over 4 years of experience building high-performance backend systems, I am excited about the opportunity to contribute to your real-time ML inference infrastructure. My expertise in Java, Python, and distributed systems aligns well with the requirements of this role.

In my previous role, I designed and implemented scalable cloud-native services using Kubernetes and AWS, processing millions of events per second with low latency. I have a proven track record of driving architectural decisions and collaborating with research teams to bring innovative solutions to production.

I am particularly drawn to Feedzai's mission of securing global commerce and the opportunity to work on cutting-edge fraud prevention technology. I am eager to apply my skills in building robust, fault-tolerant systems and contribute to your research efforts.

Thank you for considering my application. I look forward to the possibility of discussing how I can add value to your team.

Sincerely,
[Your Name]

Possible Interview Questions

Describe your experience designing and implementing a real-time machine learning inference system. What were the key challenges, and how did you address them?

In my previous role, I built a real-time ML inference system using Apache Flink and Kubernetes. The key challenges were achieving sub-100ms latency while processing millions of events per second and ensuring fault tolerance. I addressed these by optimizing data serialization, using stateful processing with checkpointing, and designing a microservices architecture with auto-scaling.

How would you approach migrating a monolithic ML serving system to a cloud-native architecture on AWS?

I would start by breaking down the monolith into microservices based on functionality (e.g., feature extraction, model inference, post-processing). Using Kubernetes and AWS EKS, I would containerize each service and implement service mesh for communication. I would leverage AWS services like S3 for model storage, DynamoDB for metadata, and Kinesis for data streaming. The migration would be done incrementally with canary deployments to minimize risk.

Explain the trade-offs between batch and real-time processing for ML inference in a fraud detection system.

Batch processing offers higher throughput and lower cost for non-urgent decisions, but introduces latency ranging from minutes to hours, which is unacceptable for fraud detection. Real-time processing provides sub-second latency but requires more resources and complex infrastructure. The trade-off can be balanced by using a hybrid approach: real-time for critical transactions and batch for less time-sensitive analysis.

Can you describe a time when you had to communicate complex research findings to non-technical stakeholders? How did you ensure clarity?

In a previous project, I developed a novel graph-based feature for fraud detection. To explain its value to product managers, I created a simplified analogy comparing it to social network analysis. I used visualizations to show how the feature improved detection rates without increasing false positives. I also prepared a one-pager summarizing the business impact and technical requirements.

How would you design a system to process 10 million events per second with strict latency and fault tolerance requirements?

I would use a stream processing framework like Apache Kafka and Flink with exactly-once semantics. The system would be deployed on Kubernetes with horizontal pod autoscaling. For fault tolerance, I would use stateful processing with checkpointing and replicas across availability zones. Data would be partitioned by transaction ID to ensure ordering. Monitoring with Prometheus and Grafana would track latency and throughput.

Feedzai is the world’s first RiskOps platform for financial risk management, and the market leader in safeguarding global commerce with today’s most advanced cloud-based risk management platform, powered by machine learning and artificial intelligence. Feedzai is securing the transition to a cashless world while enabling digital trust in every transaction and payment type. The world’s largest banks, processors, and retailers trust Feedzai to protect trillions of dollars and manage risk while improving the customer experience for everyday users, without compromising privacy. Feedzai is a Series D company and has raised $282M to date. With a valuation of $2 billion, our technology protects 1 billion consumers and 90 billion transactions each year.

Feedzai Research focuses on anticipating and solving Feedzai’s long-term goals. We strive to develop ethical cutting edge technology to stay one step ahead of the fraudsters of tomorrow. We work closely with Product and Data Science to build distributed systems that support high-load, operate 24/7 with low latencies, and use Machine Learning to make decisions and fight fraud more efficiently. As a research team, we focus on long-term, disruptive, state-of-the-art research, produce and protect our IP, publish peer reviewed work, contribute to open-source, and partner with external researchers and universities.

We are looking for someone that combines technical knowledge and research skills, is passionate about building cloud native applications that scale to process millions of events per second, while providing low-latency and fault-tolerance guarantees.

Come and change the world with us.

Your Day to Day:

Design and implement real-time Machine Learning inference systems able to process millions of events per second while achieving low-latency and fault tolerance guarantees.
Define technical standards and best practices for new cloud-native services, with particular focus on Data and ML Infrastructure.
Drive architectural decisions for Machine Learning systems workloads, balancing performance, cost, reliability, and user experience.
Be hands-on, executing the full software development life cycle and writing well-designed and testable code.
Design and develop Communicate findings and results to internal and external stakeholders, including writing scientific papers and patents.
Review and evaluate state-of-the-art research work on topics including Data Processing, Machine Learning Systems, Graphs, etc.
Own services throughout their lifecycle following DevOps principles (“you build it, you run it”).
Leverage GenAI-assisted development tools to prototype, implement, and iterate on platform capabilities quickly, while maintaining strong engineering and security standards.

You Have & You Know-how:

BSc/MSc degree in Computer Science, or a similar technical degree
4+ years of experience in developing high-performance backend services
Good understanding of distributed systems, multi-threading and OO design principles
Strong programming skills, preferable in Java (or any JVM language) and Python
Ability and interest to digest and implement state-of-art research.
Ability to communicate your findings in a clear way, transforming innovation research into product requirements.
Ability to work autonomously and take ownership of ambiguous, high-impact technical problems.
Experience with continuous delivery, monitoring, and operational ownership of services.

Preferred/Valued Qualifications and Skills:

Experience with Kubernetes and cloud-native architectures (AWS preferred).
Familiarity with Big Data technologies such as Spark, Kafka, Cassandra, EMR/Dataflow.
Knowledge of Machine Learning basics.
Experience building AI-powered products or platforms used by external customers.

#LI-BR1 #LI-Remote

Your First 30-Days at Feedzai:

You will be immersed in our brand with training, connections, and one-on-one time with your manager. You may shadow your colleagues virtually or onsite at an office depending on where you work as you are supported through your Feedzai journey. In addition, you will have access to a ton of information to give you history, context, and all the knowledge you can handle about Feedzai and the team. Finally, you will start working on projects and collaborating on work currently being done. We can’t wait to have you join the team!

Life at Feedzai Instagram

Feedzai Culture

Feedzai is an Equal Opportunity Employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Feedzai does not accept unsolicited resumes from recruiters or employment agencies.

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