Staff Software Engineer, ML/AI Platform
About the Role
We’re seeking an accomplished Staff Software Engineer to join Attentive’s Machine Learning Platform team as a high-impact individual contributor focused on building the AI and ML infrastructure that powers our AI product suite. You’ll architect and build the foundational platform components that enable AI / ML engineers and data scientists to train, deploy, and serve models and agentic infrastructure with velocity, performance, and reliability at scale.
As a Staff-level IC, you’ll operate as a technical force multiplier, setting the technical direction for AI and ML infrastructure across Attentive’s AI organization. You’ll lead through influence and technical excellence, advocating for long-term architectural progress while balancing immediate platform needs. Your work will span strategic initiatives measured in quarters and years, focusing on high-leverage decisions that enable entire teams to ship AI and ML capabilities faster and more reliably.
Strategic Need
Attentive is revolutionizing the digital shopping experience across every channel through our AI product suite for half a billion subscribers. We’re looking for a high impact individual to take our platform from v1 to vNext and beyond — supporting the full spectrum of AI and ML workloads at massive scale. We support traditional models and deep learning today, and we are growing into reinforcement learning and agentic infrastructure quickly. This is a ground-floor opportunity to drive and influence the architectural roadmap for Attentive’s entire AI and ML ecosystem toward self-service workflows, real-time inference at scale, agentic capabilities, and robust model lifecycle management.
What You’ll Accomplish
Setting Technical Direction - Architect ML platform strategy spanning data pipelines, training infrastructure, and serving layers using cutting-edge tooling like Ray, MLFlow, Metaflow, Argo, and Spark
Uplevel and Innovate Core AI & ML Stack - Build and operate production-grade, low-latency ML serving layers with robust model lifecycle systems including champion/challenger testing, automated rollouts, versioning, and rollback capabilities
Uplevel and Innovate Core AI & ML Stack - Define and drive Attentive’s agentic stack
Technical Leadership - Provide ML infrastructure perspective in high-level discussions about Attentive’s AI strategy spanning multiple quarters and teams
Technical Mentorship - Mentor platform and ML engineers, actively championing team members
Being the “Glue” - Build universal interfaces, architectures, and patterns—like data access layers and prediction serving APIs—that bridge platform capabilities with product needs to streamline high-priority ML work across the organization
Your Expertise
You have the experience to know what works, what doesn’t, and why in AI and ML systems
7+ years focused specifically on ML Platform/MLOps, with deep understanding of gold-standard practices and best-in-class tooling
Proven track record of owning and building core components of ML platforms using tools like Spark, Ray, MLFlow, Kubeflow, or Metaflow
You’ve built and operated a high-throughput agentic stack (MCP / data infrastructure, context store, orchestration, and prompt layer)
Strong expertise in Python for both batch processing and online service frameworks
Experience designing and operating online and offline inference systems, understanding the critical differences and tradeoffs between them
Sample Projects
Design and implement inference pipelines with champion/challenger shadow testing and automated model promotion
Lead and scale Attentive’s agentic stack from the ground up
Scale real-time feature streaming to handle low-latency, high-volume reinforcement learning workloads
Build a universal data access layer and prediction serving interface that powers ML capabilities across Attentive’s product suite
What We Use
Our backend is Java / Kotlin / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Aurora, AirFlow, Postgres, and Redis, hosted via AWS
Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS, using tooling like Istio, Datadog, Terraform, CloudFlare, and Helm
Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
Our automation is driven by custom and open source machine learning models, industry-leading LLMs, lots of data and tech like Python, Metaflow, HuggingFace, PyTorch, TensorFlow, and Pandas
You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.
For US based applicants:
The US base salary range for this full-time position is $200,000 - $300,000 annually + equity + benefits
Our salary ranges are determined by role, level and location
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