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!

We are looking for a machine learning engineer to design, build, experiment and optimize Shaped's internal content understanding and ranking ML stack. You will be a founding engineer that works on cutting-edge technologies and research, and applies them to impactful ranking problems. As one of Shaped’s early employees you will help shape our product roadmaps and engineering culture.

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

Qualifications

• 6+ years of industry or research experience in a machine learning, data science or AI research role
• Previous experience working with recommendation systems in production
• Previous experience in NLP or Computer Vision or other adjacent AI fields
• Proficient with Python, SQL
• Have the willingness to wear many hats, be passionate about unblocking issues for customers, and have an excitement for problem-solving
• In-depth knowledge of mathematics, statistics and algorithms.
• Operate well within fast paced, small teams
• Excellent written and verbal communication skills

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.