Skills: Machine learning, Python, SQL, Data Modeling

We're looking for a customer-facing developer to help customers integrate and develop recommendation models using the Shaped Platform. Responsibilities include connecting external data sources to Shaped, creating ranking models with our declarative SQL interface, and helping customers achieve their business objectives.

As a founding solutions architect, you'll collaborate with a diverse range of customers to solve their ranking challenges. Additionally, you'll contribute to shaping our product roadmaps and engineering culture.

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

Qualifications

• Bachelor's in computer science, data science or mathematics related field. Master's degree or PhD will be advantageous.
• 3+ years of industry experience in a software engineering, data science or solutions architecture role
• 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.

Responsibilities

• Become an expert at using the Shaped ranking platform
• Experiment with ranking models for customer's discovery use-cases
• Analyze data to identify performance improvements for customers' models swiftly
• Experiment with pre-trained image, language, audio and video embedding models that understand our customer's discovery data
• Build strong relationships with customers' technical teams and leaders to ensure long-term success with Shaped
• Deliver technical demos to showcase the Shaped's capabilities to customers.
• Proactively communicate with customers on timelines, updates and next steps

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.