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!

Machine Learning Engineer - Job post

👋  Hello! We’re building the future of content discovery infrastructure with AI — come join us!

Shaped (YC W22) is an API for developers to seamlessly add personalized ranking and recommendation to their products. These frictionless discovery experiences help end-users find what they want faster, and grow conversion and engagement business metrics. Behind the scenes, we’re building state-of-the-art ML infrastructure for training and deploying large AI models to power our personalization engine.

We’re a seed stage start-up backed by top-tier investors (e.g. Y-Combinator, Susa Ventures, Tribe Capital etc…) and executives (e.g. Google, Amazon, Uber, Dropbox etc…). Our team comes from top companies like Meta, Google, Apple and Uber. We’re a remote team but have a small office in Brooklyn, New York we try to centralize around.

We are looking for a machine learning engineer to design, build and optimize Shaped's ranking engine. You will be a founding engineer working on state-of-the-art machine-learning infrastructure and models. You will have the chance to touch all parts of the production and experimentation ML stack. As one of Shaped’s early employees you will help shape our product roadmaps and engineering culture.

This role is for a full-time engineer at $130k - $160k a year and 0.25% - 0.75% equity.

Responsibilities:

• Experiment and develop predictive ranking models for customer's discovery use-cases.
• Experiment and develop with pre-trained image, language, audio and video embedding models that understand our customer's discovery data.
• Build the infrastructure to train and deploy Shaped's embedding and ranking models.
• Implement data validation and logging infrastructure for monitoring our production models.
• Optimize machine-learning libraries and accelerate models to serve real-time inference.
• Work on our real-time and batch data ingestion pipelines using modern data engineering pipelines/streams.
• Work closely with a small team to inform the roadmap and co-develop a strong engineering culture with best practices.

Requirements:

• Bachelor's in computer science, data science or mathematics related field. Master's degree or PhD will be advantageous.
• 3+ years of machine learning engineering industry experience.
• Proficient with Python, SQL.
• Extensive knowledge of ML frameworks, modeling, and data engineering tools.
• In-depth knowledge of mathematics, statistics and algorithms.
• Excellent written and verbal communication skills.

We’re excited to work, collaborate, grow, teach, and learn from you! Come build the future of AI with us!

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.