Staff Software Engineer, ML Engineering


We switched to a 4 day work week in January 2022 (32hrs / week)

Only considering candidates eligible to work in Budapest, Hungary ⚠️

Who Are You

We seek a highly skilled and experienced Staff Software Engineer to join our dynamic and growing ML Engineering team. As a Staff Software Engineer for ML Engineering, you will build platforms that empower fellow engineers and Data Scientists to create market-leading fraud prevention products. We want you to help us lead the scaling of our business, make data-driven decisions, and contribute to our overall ML and data strategy. The ideal candidate must:

  • Effectively communicate complex data problems by tailoring the message to the audience and presenting it clearly and concisely.
  • Balance multiple perspectives, disagree, and commit when necessary to move key company decisions and critical priorities forward.
  • Ability to work independently in a dynamic environment and proactively approach problem-solving.
  • Be committed to driving positive business outcomes through expert data handling and analysis.
  • Be an example for fellow engineers by showcasing customer empathy, creativity, curiosity, and tenacity.
  • Have strong analytical and problem-solving skills, with the ability to innovate and adapt to fast-paced environments.
  • Design and build clear, understandable, simple, clean, and scalable solutions.

What You'll Do

  • Be an individual contributor who can navigate between design/architecture and execution of the design.
  • Modernize Signifyd’s Machine Learning (ML) Platform to scale for resiliency, performance, and operational excellence, working closely with Engineering and Data Science teams across Signifyd’s R&D group.
  • Work alongside ML Engineers, Data Scientists, and other Software Engineers to develop innovative big data processing solutions for scaling our core product for eCommerce fraud prevention.
  • Take full ownership of significant portions of our ML processing products, including collaborating with stakeholders on machine learning models, designing large-scale data processing solutions, creating additional processing facets and mechanisms, and ensuring the support of low-latency, high-quality, high-scale decisioning for Signifyd’s flagship product.
  • Own the significant advancement of our model training and data processing ecosystem, including the evolution of our feature stores and data processing pipelines.
  • Architect, deploy, and optimize ML and Data solutions on AWS, developing scalable data processing solutions to streamline data operations and analysis and enhance data solution deployments.
  • Implement data and ML processing solutions for offline, batch, and real-time use cases.
  • Mentor and coach fellow engineers on the team, fostering an environment of growth and continuous improvement.
  • Identify and address gaps in team capabilities and processes to enhance team efficiency and success.
  • Automate monitoring of model performance and user behavior.
  • Influence the tooling, frameworks, and ML practices with the ML teams.

What You'll Need

  • Ideally has 5-10 years of experience in data/ML engineering, including at least five years of experience as a software or machine learning lead. Have successfully navigated the challenges of working with large-scale data processing systems.
  • Deep understanding of data processing, comfortable working with multi-terabyte datasets, and skilled in high-scale data ingestion, transformation, and distributed processing, with strong Apache Spark experience.
  • Experience in building low-latency, high-availability data stores for use in real-time or near-real-time data processing with programming languages such as Python, Scala, Java, or JavaScript/TypeScript, as well as data retrieval using SQL and NoSQL.
  • Hands-on expertise in data technologies with proficiency in Spark, Airflow, Databricks, AWS services (S3, EMR, SQS, Kinesis, etc.), and Kafka. Understand the trade-offs of various architectural approaches and recommend solutions suited to our needs.
  • Significant experience in programming languages such as Java, Python, or Scala and experience understanding Cloud infrastructure environments including Kubernetes and Serverless.
  • Working knowledge of ML algorithms, clustering algorithms, and binary classifiers (such as XGBoost)
  • Experience using feature stores: homegrown solutions or commercial and open-source products like Tecton and Chronon.
  • Experience with the latest technologies and trends in Data, ML, and Cloud platforms.
  • Demonstrable ability to lead and mentor engineers, fostering their growth and development.
  • You have successfully partnered with Product, Data Engineering, Data Science, and Machine Learning teams on strategic data initiatives.



  • Stock Options
  • Annual Performance Bonus or Commissions
  • Pension matched up to 3%
  • ‘Day one’ access to great health insurance scheme
  • Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads)
  • Paid team social events
  • Mental wellbeing resources
  • Dedicated learning budget through Learnerbly

Signifyd's Applicant Privacy Notice

Optimize Revenue + Automate Orders + Eliminate Fraud

Working Week

We switched to a 4 day work week in January 2022 (32hrs / week)

  • Mon
  • Tue
  • Wed
  • Thu
  • 🏖️

Our Vacation Policy

Our vacation policy varies by location e.g. in the USA we offer Discretionary Time Off Policy (unlimited) and in the UK we offer 20 days PTO plus public holidays. For the UK this works out as:

  • 28 days
  • 52 Fridays
  • 80 days off per year

Remote Working Policy

We have offices in San Jose, New York, Denver, Belfast, London, Sao Paulo and Mexico City. Many of us also work 100% remotely.

Company Benefits

  • Health insurance
  • 401K Match
  • Generous parental leave
  • Dentalcare
  • Equity / options
  • Equipment allowance

Our Team

We're a team of 550 across 19 departments:

  • engineering
  • data science
  • sales
  • support
  • operations
  • business development
  • marketing
  • +12 more teams

Desirable Skills and Experience

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