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!

As the Head of Engineering at Shaped, you will be a pivotal member of our leadership team, responsible for scaling our engineering organization and driving the technical vision of our products. You'll lead a team of talented engineers, fostering a culture of innovation, collaboration, and excellence. Your leadership will be instrumental in shaping our product roadmap, ensuring the reliability and scalability of our platform, and aligning engineering efforts with business objectives.

Responsibilities

• Technical Leadership: Define and drive the technical strategy, ensuring alignment with Shaped's mission and product goals.
• Team Management: Recruit, mentor, and retain top engineering talent, fostering a culture of continuous learning and improvement.
• Product Development: Oversee the design, development, and deployment of scalable, reliable, and secure systems.
• Cross-Functional Collaboration: Work closely with Product, Sales, and Customer Success teams to deliver features that meet customer needs.
• Process Optimization: Implement best practices in software development, including agile methodologies, code reviews, and testing.
• Infrastructure Oversight: Ensure the robustness of our cloud infrastructure, focusing on scalability, observability, and automation.

Qualifications

• Educational Background: B.S., M.S., or Ph.D. in Computer Science or a related technical field.
• Experience: 8+ years in software engineering
• Soft Skills: Excellent communication skills, with the ability to articulate technical concepts to non-technical stakeholders.
• Problem-Solving: Strong analytical skills and a track record of making data-driven decisions.

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.