Engineering Manager

Signifyd

3d•$200,000 - $235,000•Remote

About The Position

Signifyd uses the latest in AI and machine learning technology to give our ecommerce customers the confidence they need to do business free from fears of fraud and other forms of ecommerce abuse. In order to do this we have to continually innovate and nowhere is this more important than in our modeling space. The modeling group creates, deploys and runs the models and services that keep us a step ahead and right now we need an engineering manager to help us build our next generation technology. The successful candidate will lead a team of engineers who will partner with our AI group and engineering as a whole to help bring ideas from conception to production, while ensuring the systems we already have continue to run smoothly. It’s a role for someone who can operate comfortably on both sides of the technical/business line, contributing to technical discussions but people-focused enough to build the relationships between engineering, our AI lab, product and our risk management organization. You will work daily with data scientists, machine learning engineers, product owners and other cross-functional stakeholders whose work depends on your team's systems. Success means being a trusted translator and partner across all of these groups, not just a manager of your own team's output. Your core responsibilities are delivery, fostering innovation and people leadership. How hands-on you get as part of that is up to you but you will be expected to drive closure on technical decisions and implementation.

Requirements

5+ years working as a software engineer. Some of that experience should be in the ML sphere.
At least 4 years of experience managing engineers directly in an ML focused domain.
Comfortable engaging in technical discussions on architecture, data flows, and system design.
Demonstrable experience using AI tools in a professional capacity for research, administration and code generation with a clear point of view on how to use them responsibly and cost-effectively.
Strong knowledge of software development best practices, cloud computing platforms, big data technologies, MLOps toolkits, and SLO-driven reliability management.
Strong track record managing relationships with a diverse set of technical and non-technical stakeholders.
Ability to communicate clearly across audiences and influence without authority.
Comfortable navigating competing priorities and pushing back constructively when needed.
Genuine care for engineer growth and development, with experience giving structured feedback and building career paths.
Ability to build trust and psychological safety within the team while holding a high bar for output and accountability.

Nice To Haves

Experience working with at least one of Databricks, Spark, Airflow, Vertex and the GCP technology stack in general is preferred.

Responsibilities

Manage, coach and grow a team of engineers, providing regular feedback, career development while ensuring performance remains high.
Build a healthy team culture with clear ownership, sustainable pace and high standards for quality and craftsmanship.
Recruit and onboard new engineers as needed and develop technical leadership within the team.
Manage competing priorities and expectations across multiple stakeholder groups, building trust through fostering mutual understanding, delivering consistently and communicating transparently.
Represent the team's roadmap, capacity and risks in cross-functional planning and prioritization discussions.
Drive technical discussions and design reviews, with enough depth to understand the implications of key decisions on reliability, scalability and production outcomes.
Partner with engineers on architecture and design choices, asking the right questions rather than dictating solutions.
Ensure the team's technical decisions are well understood by, and defensible to, other stakeholders.
Model and champion effective use of AI tools across the team's day-to-day work, including AI-assisted research, administrative workflows, and code generation.
Help the team develop good judgment about how to balance the productivity gains AI can bring against the risks it introduces.
Continuously look for ways AI tooling can improve team efficiency, code quality, and decision-making, and share what's working with peers.
Manage effectively through the major changes AI is bringing to the industry.
Own delivery of the team's roadmap balancing feature work, technical debt and reliability commitments.
Establish and maintain appropriate quality, testing and monitoring practices.
Provide clear, proactive communication on progress, risks and blockers to stakeholders and leadership.
Define metrics as needed and develop an understanding of why we did or didn’t hit our targets.