Skills: Figma

We're looking for a staff level designer to help shape the future of Shaped. This work will require excellence in systems thinking and understanding the needs of our two primary users: engineers looking to work with our API and companies looking to gather insights from their use of our product.

• Primary responsibility is to lead the design for the interface of the Shaped machine-learning platform which allows developers to create bespoke AI models for recommendation system algorithms and understand their users data
• In this role you'll be working 1:1 with the founders of Shaped to design product features needed over the next few sprints as well as craft a long term vision for the future of the company
• This is not a role that requires pixel perfect designs created over 3 months — it requires a sense of when to move fast and slow so we can scale the company as a team

Qualifications

• Open to wearing multiple hats occasionally: getting product design feedback directly from customers, designing graphics for marketing content, or helping with a blog post
• Experience working directly and closely with engineers and or data teams
• 6+ years of industry experience in a product design role
• Operate well autonomously within a fast paced environment
• B2B, SaaS, developer tools, data, analytics tools (Segment, Amplitude.) or AI design experience advantageous

Technology

Customers typically use Shaped as follows:

1. Connect your data stack, e.g. data warehouse, database or analytics applications
2. Define your model. This includes your optimization objective (e.g. clicks vs purchases vs shares), item and user catalogs, feature types and model types.
3. Consume your results from our real-time, scalable ranking endpoints
4. Evaluate uplift and model results on our dashboard.

To power all of this, under the hood, we've built a multi-tenanted, real-time machine learning architecture which automatically sets-up and ingests data both in real-time and batch, transforms data and stores it into our proprietary feature/vector store. Ranking models are continuously optimized and fine-tuned based on real-time feedback ensuring customers are seeing the most relevant and up-to-date results possible.

From a machine-learning perspective we use state-of-the-art large scale neural encoding models to understand multi-modal data types such as image, text, audio and tabular data. We provide an exhaustive library of retrieval, ranking and ordering algorithms which are selected based on the specified model definition.

We use both AWS and GCP for cloud. Kubernetes for serverless infrastructure. Python, Javascript and Rust for languages.