Software Engineer II, Machine Learning Platform

Attentive® is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to deliver 1:1 messages that truly resonate.

With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like Neiman Marcus, Samsung, Wayfair, and Dyson, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more or request a demo, visit www.attentive.com or follow us on LinkedIn, X (formerly Twitter), or Instagram.

Attentive’s growth has been recognized by Deloitte’s Fast 500, Linkedin’s Top Startups and Forbes Cloud 100 all thanks to the hard work from our global employees!

About the Role

We’re looking for a self-motivated, highly driven Software Engineer II to join our Machine Learning Platform (MLOps) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, inference, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform spanning the full ML lifecycle for consumption by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing ML Platform team at the ground floor with the ability to drive and influence the architectural roadmap enabling the entire ML organization at Attentive.

This team and role is responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack.

• Expand, mature, and optimize our ML platform built around cutting edge tooling like Ray, MLFlow, Argo, and Kubernetes to support traditional and deep learning ML models
• Build and mature capabilities to support CPU / GPU clusters, model performance monitoring, drift detection, automated roll-outs, and improved developer experience
• Build, operate, and maintain a low-latency, high volume ML serving layer covering both online and batch inference use cases
• Orchestrate Kubernetes and ML training / inference infrastructure exposed as an ML platform
• Expose and manage environments, interfaces, and workflows to enable ML engineers to develop, build, and test ML models and services
• Close the latency gap on model inference to online, real-time model serving
• Develop automation workflows to improve team efficiency and ML stability
• Analyze and improve efficiency, scalability, and stability of various system resources
• Partner with other teams and business stakeholders to deliver business initiatives
• Help onboard new team members, provide mentorship and enable successful ramp up on your team's code bases

• You have been working in the areas of MLOps / Platform Engineering / DevOps / Infrastructure for 3+ years, and have an understanding of gold standard practices and best in class tooling for ML
• Your passion is exposing platform capabilities through interfaces that enable high performance ML practices, rather than designing ML experiments (this team does not directly develop ML models)
• You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs
• You have experience building infrastructure for an ML platform and managing CPU and GPU compute
• You have a background in software development and are passionate about bringing that experience to bear on the world of ML infrastructure
• You have experience with Infrastructure as Code using Terraform and can’t imagine a world without it
• You understand the importance of CI/CD in building high-performing teams and have worked with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCD
• You are passionate about observability and worked with tools such as Splunk, Nagios, Sensu, Datadog, New Relic
• You are very familiar with containers and container orchestration and have direct experience with vanilla Docker as well as Kubernetes as both a user and as an administrator

• You have been working in the areas of ML Platform / MLOps / Platform Engineering / DevOps / Infrastructure for 3+ years, and have an understanding of gold standard practices and best in class tooling for ML
• Your passion is exposing platform capabilities through interfaces that enable high performance ML practices, rather than designing ML experiments (this team does not directly develop ML models)
• You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs
• You have experience building infrastructure for an ML platform and managing CPU and GPU compute
• You have a background in software development and are passionate about bringing that experience to bear on the world of ML infrastructure
• You have experience with Infrastructure as Code using Terraform and can’t imagine a world without itYou understand the importance of CI/CD in building high-performing teams and have worked with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCD
• You are passionate about observability and worked with tools such as Splunk, Nagios, Sensu, Datadog, New RelicYou are very familiar with containers and container orchestration and have direct experience with vanilla Docker as well as Kubernetes as both a user and as an administrator.

• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
• Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
• Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
• 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, lots of data and built with 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 $148,000 - $195,000 annually + equity + benefits

- Equity is a substantial part of the total compensation package

- Our salary ranges are determined by role, level, and location

#LI-EZ1

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.