Growth Engineer at Shaped

Shaped is the fastest path to relevant recommendation and search systems. We help companies turn their behavioral data into truly relevant product and website experiences.

We're a Series A company based in Brooklyn, New York and backed by top investors from Madrona, Y-Combinator, and executives from Meta, Google, Amazon and Uber!

Growth Engineer

We are looking for a creative and technical Growth Engineer to connect Shaped with the developer community. You will be responsible for building compelling open-source demos and creating technical content that showcases the power of Shaped’s recommendation and search platform. You will be the bridge between our product and the developers who use it, playing a crucial role in driving adoption and gathering community feedback. As one of Shaped’s early employees, you will help shape our growth strategy and engineering culture.

We’re excited to work with you. Come build the future of AI with us!

Qualifications

• 3+ years of experience in a software engineering, developer advocacy, or technical growth role.

• Proven experience building full-stack applications or interactive demos with Python and modern web frameworks (e.g., Next.js/React, Streamlit, Gradio, FastAPI).

• Excellent written and verbal communication skills, with a talent for explaining complex technical concepts clearly and concisely.

• Experience creating technical content such as blog posts, tutorials, or documentation.

• A passion for engaging with developer communities and a deep sense of empathy for the developer experience.

• Familiarity with or a strong interest in AI/ML APIs, recommendation systems, or search technology.

• Operate well within fast-paced, small teams and have an excitement for creative problem-solving.

• Willingness to wear many hats and be a proactive self-starter. Bonus Points

• Previous experience in a developer relations or advocacy role.

• A portfolio of personal projects, open-source contributions, or technical blog posts.

• Experience speaking at meetups, conferences, or hosting online events. Customers typically use Shaped as follows:

• Connect your data stack, e.g. data warehouse, database or analytics applications

• Define your model. This includes your optimization objective (e.g. clicks vs purchases vs shares), item and user catalogs, feature types and model types.

• Consume your results from our real-time, scalable ranking endpoints

• 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.