Manager, Software Engineering (ML Inference)

TL;DR

Manager, Software Engineering (ML Inference): Leading and mentoring a team of ML infrastructure engineers to build and scale systems for model training, inference, and data pipelines with an accent on high-availability, distributed systems, and operational excellence. Focus on setting technical strategy, driving high-impact infrastructure initiatives, and fostering the growth of high-performing engineering teams.

Location: Must be based in the US and work from an office 4+ days per week in Bellevue, Los Angeles, or Palo Alto.

Company

Snap Inc. is a technology company focused on visual messaging and camera-based products that empower people to express themselves and connect with the world.

What you will do

• Lead and mentor a team of ML infrastructure engineers responsible for scaling model training and inference systems.
• Define technical strategy, build roadmaps, and establish measurable goals for ML infrastructure initiatives.
• Perform design and code reviews to maintain high standards for technical excellence, security, and performance.
• Collaborate with ML engineers and cross-functional stakeholders to deliver scalable solutions.
• Hire, retain, and develop high-performing engineers through regular feedback and growth opportunities.
• Advocate for best practices in availability, scalability, and cost management.

Requirements

• Bachelor's degree in a technical field or equivalent experience.
• 9+ years of software engineering experience (or 8+ years with a Master's, 5+ years with a PhD).
• 1+ year of experience managing an engineering team.
• Strong understanding of ML infrastructure, including inference serving, feature stores, and data pipelines.
• Experience with distributed systems and large-scale ML infrastructure.
• Must be able to work in the office 4+ days per week.

Nice to have

• Advanced degree in a related technical field.
• Experience with ML frameworks like TensorFlow, PyTorch, or Spark ML.
• Familiarity with big data technologies such as Spark, Flink, or Ray.
• Experience with infrastructure tools including Kubernetes, NoSQL, Redis, Kafka, and cloud platforms (GCP/AWS).
• Experience with MLOps and production machine learning lifecycles.

Culture & Benefits

• Comprehensive medical coverage and emotional/mental health support programs.
• Paid parental leave.
• Compensation packages designed to share in long-term company success.
• Commitment to diversity, equity, and inclusion as an equal opportunity employer.
• Collaborative environment emphasizing precision, privacy, and fast-paced execution.