Technical Lead/Manager - ML team

Our Team:

Our ML teams are part of our core Data Science and Machine Learning group and consist of Online and Offline ML teams.

The Online ML team is responsible for building and operating low-latency and data-intensive systems such as a feature store, feature extraction, ML model serving, and versioning systems. 

The Offline ML team is responsible for ML model release process, ML pipelines, model training, and validation.

What we’re looking for:

As the Technical Lead, you will start with individual technical contributions and later will take an engineering manager role for the hiring team.

During onboarding, you will work on features technical design, implementation, and collaboration with engineering managers and engineers from US-based teams. After your onboarding, we are going to hire engineers in Ukraine for our ML teams and you will take the engineering manager role for this team.

You value collaboration, transparency, and a Get Stuff Done mindset. As a leader, you understand the importance of these aspects of engineering success and lead by example. We are firm believers in servant leadership. 

Tech stack:

- Java

- GCP

- VertexAI

- Flink

- Dataflow

- Dataproc

- Airflow

Opportunities for you:

• Professional growth: quarterly Growth Cycles instead of performance review;
• Experience: knowledge sharing through biweekly Tech Talks sessions. You will learn how to build projects that handle petabytes of data and have small latency and high fault tolerance;
• Business trips and the annual Sift Summit, in 2022, Summit took place in California;
• Remote work approach: you can choose where you work better.

What would make you a strong fit:

• 2+ years of hands-on experience managing BigData/backend teams and 7+ years of professional software development experience;
• Experience building highly available low-latency systems using Java, Scala, or other object-oriented languages;
• Knowledge of GCP or AWS cloud stack for web services and big data processing;
• Basic knowledge of MLOps on model release/training/monitoring;
• Conceptual knowledge of ML techniques;
• Experience initiating cross-functional collaboration between multiple functions (technical and non-technical);
• Experience hiring, mentoring, and developing top engineering talent;
• B.S. in Computer Science (or related technical discipline), or related practical experience.

Bonus points:

• Experience working with large datasets and data processing technologies for both stream and batch processing, such as Apache Spark, Apache Beam, Flink, and MapReduce;
• Experience in management and project management skills for planning and executing complex projects;
• Experience solving problems with production systems, and building solutions and automation to prevent them from reoccurring;
• Familiarity with practical challenges in ML systems such as feature extraction and definition, data validation, training, monitoring, and management of features and models;
• Practical knowledge of how to build end-to-end ML workflows;
• Experience with building an ML feature store for batch and real-time aggregation/serving.

What you’ll do:

• Building and operating low-latency and data-intensive systems;
• Designing, implementing, and operating large-scale distributed systems;
• Execute and improve the ML model release process;
• Collaborate with US-based ML teams and work with them in close partnership on core components of Sift products;
• Lead, manage, and mentor a team of talented, broadly-capable engineers who own key areas of our fraud detection platform, as well as systems that deliver unique, strategic insights in a challenging domain;
• Collaborate with other engineering teams to ensure we are working effectively and focusing on the right priorities;
• Help identify key strategic areas for investment in both product and technical backlogs;
• Demonstrate and embody a strong, abiding commitment to the highest cultural standard, focusing on inclusivity, collaboration, and teamwork.

A little about us:

Sift is the leading innovator in Digital Trust & Safety.  Hundreds of disruptive, forward-thinking companies like Doordash, Binance, and Twitter trust Sift to deliver an outstanding customer experience while preventing fraud and abuse.

Sift is a series E company with a valuation of $1.7 billion as a unicorn. In 2021, Sift acquired 2 startups: Chargeback and Keyless to extend the company's product portfolio. Sift was nominated as the Best Employer in 2020 in Seattle.

Sift is a big data ML-based platform that processes 70B API requests per month, processes 1PB of data, and tens of thousands of transactions per second.