Machine Learning Engineer

About Us

Riskified empowers businesses to unleash ecommerce growth by taking risk off the table. Many of the world’s biggest brands and publicly traded companies selling online rely on Riskified for guaranteed protection against chargebacks, to fight fraud and policy abuse at scale, and to improve customer retention. Developed and managed by the largest team of ecommerce risk analysts, data scientists and researchers, Riskified’s AI-powered fraud and risk intelligence platform analyzes the individual behind each interaction to provide real-time decisions and robust identity-based insights. Riskified is proud to work with incredible companies in virtually all industries including Wayfair, Acer, Gucci, Lorna Jane, GoPro, and many more.

We thrive in a collaborative work setting, alongside great people, to build and enhance products that matter. Abundant opportunities to create and contribute provide us with a sense of purpose that extends beyond ourselves, leaving a lasting impact. These sentiments capture why we choose Riskified every day. 

Our Data Science Team

• We are focused on bringing value to Riskified through the development of models and analytical solutions across domains. We use a wide variety of advanced techniques and algorithms to provide maximum value from data in all shapes and sizes: classic ML and deep learning, supervised and unsupervised, NLP, anomaly detection, graph theory, and more. 
• We use the most cutting-edge solutions - from event driven (Kafka), to big data solutions (Spark, Datalake, Databricks), Cloud operations (Docker & Kubernetes), Workflow orchestration (Airflow), and Machine Learning Platforms (Databricks & Kubeflow), coding mostly in Python, Scala, and R.
• We’re a friendly, fun and diverse team. We’re passionate about making data-driven decisions being open-minded and creative, while communicating openly and honestly. We thrive in a continuous learning culture to promote growth and remain at the forefront of technological innovation.

About the Role

In this role, you'll merge production grade software engineering, data engineering, data science expertise and MLOps proficiency, developing and implementing advanced analytical solutions for real-world applications. Your focus will be on constructing the infrastructure for the training of the company’s machine learning models - from both computational aspects and analytical validity. Through collaboration with cross-functional teams, you'll integrate these ML capabilities seamlessly into our product ecosystem, driving efficient, data-driven outcomes.

What You'll Be Doing

 

Developing Advanced Analytical Solutions while bridging the gap between data science and the engineering world: You will be instrumental in designing and implementing production-grade analytical tools and systems. Your work will involve transforming complex data science concepts into practical, scalable solutions that drive real-world results.

Facilitating Data Science Excellence through MLOps Frameworks: In this vital role, your primary objective is to gain a deep understanding of the needs and challenges faced by our Data Science team. Utilizing this insight, you will build or leverage existing MLOps solutions to maximize our business impact. This involves bridging the gap between Data Science and Business Objectives, implementing scalable MLOps frameworks, adopting an example-driven approach, enhancing Data Science productivity, and fostering collaborative innovation.

Qualifications

• At least 4 years of experience as a Machine Learning Engineer/ combination of a Software Developer and a Data Scientist 
• BSc in Computer Science or similar. M.Sc/Ph.D - Advantage
• Experience in developing ML solutions successfully, from data analysis to evaluation and deployment 
• Experience in building production grade services, with a good understanding of scale, distributed systems, and up-to-date software development concepts (CI/CD, dockerization & more)
• Proficiency in data engineering - specifically in spark, as well as other distributed processing engines, datalake platforms, etc
• Excellent communication skills with the ability to clearly explain complex concepts to business stakeholders
• Demonstrated experience in the application of MLOps frameworks and tools - Advantage

• Familiarity with Large Language Model (LLM) frameworks - Advantage

If you are a passionate ML Engineer with a strong background in machine learning and a desire to make a significant impact with your analytical skills, we would love to hear from you. Join our team and be a part of driving data-driven decision-making at Riskified.

Life at Riskified

We are a fast-growing and dynamic tech company with 750+ team members globally. We value collaboration and innovative thinking. We’re looking for bright, driven, and passionate people to grow with us. 

COVID-19 Update: 

• Our Tel-Aviv team is currently working in a hybrid of remote and in-office work. We have recently moved to our new space in Tel Aviv - check it out here! 

Some of our Tel Aviv Benefits & Perks:

• Equity for all employees, Keren Hishtalmut, pension
• Private medical insurance, extra time off for parents and caregivers
• Commuter and parking benefits
• Team events, fully-stocked kitchen, lunch stipend, happy hours, yoga, pilates, functional training, basketball, soccer
• Wide-ranging opportunities to volunteer and make an impact
• Commitment to your professional development with global onboarding, skills-based courses, full access to Udemy, lunch & learns
• Awesome Riskified gifts and swag! 

In the News

Geektime: Riskified Goes Public

Walla!: Happy Hour at the Riskified Offices

Geektime Insider: A look at Riskified Tel Aviv

Globes: Riskified to contribute the highest amount up to date to Tmura

Globes: Riskified is among Israel’s fastest growing companies 

TechCrunch: Riskified Prevents Fraud on Your Favorite E-commerce Site

Riskified is deeply committed to the principle of equal opportunity for all individuals. We do not discriminate based on race, color, religion, sex, sexual orientation, national origin, age, disability, veteran status, or any other status protected by law.