When we started in 2014, our focus was on product managers in smaller teams who lacked a great product management tool. Today, we are the leading product management platform, and as we continue to grow, we are now helping more and more enterprise companies with thousands of employees to build products that matter.

We believe Productboard's greatest differentiator is enabling our customers to collect, understand, and act upon data from the market and empower them to organize them into Insights, understand their customers, and align their company around the most impactful opportunities.

We are in search of an experienced Machine Learning Engineer to become a member of our cross-functional team. Your role will span across the entire AI feature development lifecycle.

About the AI team

AI team is a cross-functional product team, composed of backend, frontend, machine learning engineers with PMs and designers. You might have already explored the AI capabilities within our product, and we aim to build more copilot features into Insights and other domains, enabling bulk-processing, pre-processing, smart suggestions and other functionality. In the end, our customers would be presented with aggregated insights & clustered data, which is connected to the features & ideas they plan to build.

Where are we heading?

To create even more value for customers, we are on a mission to help product teams transform their product management practices, leveraging the power of AI to make impactful product decisions faster through a deeper understanding of different types of data within the Productboard platform (customer feedback, competitors, market intelligence, business strategy, etc.)

We also aim to make product teams more effective on tactical and strategic tasks throughout the entire product management lifecycle with AI-powered workflows that augment existing core workflows within Productboard.

Our core challenges:

• Introducing new capabilities into our solutions – a feature store, eliminating the need of manual processing of incoming Insights data, live predictions, co-usage of different vendor LLMs, etc.
• Ensuring data flows from the frontend via GraphQL – asynchronous processing through Kafka in different services, and back to frontend – produce a seamless UX experience
• Ensuring our core services, which store data from different sources, are ready to scale and meet performance requirements

We’re looking for experienced engineering minds, who are able to not only lead big technical projects, but act as knowledge multipliers inside and outside of the team. Those who strive to build top-end services and are able to turn a good system design on paper into a well-tuned working solution.

Our tech stack

You’ll work with the following frameworks, tools, and languages:

• We write our ML code in Python
• When it comes to scheduling ML pipelines, we rely on orchestration frameworks like Airflow 2+ and Kubernetes
• Our real-time services run on AWS and Kubernetes, backed by Git, CI/CD, Docker, Helm, and Kafka
• For monitoring our services we use Datadog and Sentry, and for a business overview, we've got Looker in our toolkit
• Our tech toolbox also includes GraphQL and Postgres, among other technologies

About you

We are currently seeking an individual who possesses the following skills and qualities:

• Professional expertise in building Python applications
• Proficiency in designing, executing, and maintaining ML systems and solutions in a production environment
• Familiarity with the management of performance and testing of ML systems
• Practical experience with message queue systems and a grasp of event-driven architecture
• A background in data science and LLMs would be highly advantageous

You will help us with:

• Building AI-powered product features
• Enhancing and sustaining our internal tech stack, while identifying and incorporating new state-of-the-art technologies
• Discovering and experimenting across different domains, creating MVPs and POCs, engaging in discussions about findings with fellow engineers and the product team, and planning the execution
• Collaborating with other engineers, introducing fresh concepts and methodologies to the team

The team has recently worked on:

• Delivering LLM solutions to our customers
• Establishing a Feature store dedicated to storing embeddings for our services
• Training NLP models, utilized by numerous customers, with daily retraining cycles
• You can check our recent AI releases at https://www.productboard.com/product/ai/