Senior Engineering Manager, Ad Performance Recommendation

About the role

This position defines strategy and hands-on leadership for an Ad Performance Recommendation team inside a large product development organization.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

AdTech-specific ML models and foundational AI capabilities are developed, optimized for performance, efficiency, and scalability across Smartly's products.

An engineering culture that values innovation, quality, and continuous improvement is built and maintained.

Obstacles are removed so the team can thrive and deliver exceptional results.

Recruiting is partnered with to actively source and close top talent as the team scales.

The long-term AI strategy and architecture for Smartly are shaped together with other AI Platform leaders.

Requirements

7+ years of experience in building services and products is required.

5+ years of experience in applying machine learning to real-life problems is required.

3+ years of experience in leading teams is required.

Core ML technologies, including PyTorch, TensorFlow, Python, and SQL, must be handled competently.

Mentoring, coaching, and developing data scientists and machine learning engineers is expected.

Delivery of scalable ML systems in production is proven, including hands-on coaching such as code reviews and pair programming.

A pragmatic mindset that focuses on creating customer value in production systems is necessary.

Curiosity and the drive to continuously learn through study and experimentation on the job are needed.

Technical excellence and cross-functional collaboration must be driven.

Understanding of security and compliance requirements when working with personal data is required.

M.Sc. or Ph.D. in a relevant field such as Mathematics, CS, AI, or Physics is required.

Excellent written and spoken English is required.

Ability to work 3 days per week at the office is required.

Practical notes

The role operates within a 200+ product development organization in the AI Platform group.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Machine learning models turn marketing ad tech data into recommendations for advertisers.

Core tools include PyTorch, TensorFlow, Python, and SQL for model development.

The company holds leadership recognition in ad technology and collaborates with major platforms and brands.

The role follows a flexible hybrid work policy.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.