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

#LI-EZ1

By applying for this position, your data will be processed as per Attentive's Privacy Policy.

Attentive Company Values Default to Action - Move swiftly and with purpose Be One Unstoppable Team - Rally as each other’s champions Champion the Customer - Our success is defined by our customers' success Act Like an Owner - Take responsibility for Attentive’s success Learn more about AWAKE, Attentive’s collective of employee resource groups. If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience. At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide equal employment opportunities for all employees, applicants and covered individuals regardless of protected characteristics. We prioritize and maintain a fair, inclusive and equitable workplace free from discrimination, harassment, and retaliation. Attentive is also committed to providing reasonable accommodations for candidates with disabilities. If you need any assistance or reasonable accommodations, please let your recruiter know.