Address
Form of work
SalaryTL/DR: You ship fast, maintain a high bar for quality, will not tolerate those who don’t.
This role will be a Founding Full Stack Engineer who reports directly to our Lead Engineer focused on backend development. As a Founding Engineer on our core team, you’ll be responsible for building out the core product: LinkedIn & email integrations, data enrichments, web scraping, and leveraging nascent LLM tooling to perfect our cloning technology to truly capture the essence of how a sales rep would engage with different prospects.
Being in NYC & willing to work hybrid is required. We will be hiring key team members in NYC moving forward. The engineering team is remote but working EST hours.
Tech Stack:
- TypeScript, Node.js
- Frameworks and Tools - Nx monorepo, Git, ESLint, Jest
- Frontend specific - Nextjs (pages router), shadcn, Relay GraphQL Client, Playwright
- Backend specific - Nestjs, Pothos GraphQL, Fastify, BullMQ
- DB - Postgres with Prisma ORM, Redis
- Platforms - Vercel, DigitalOcean
- Python - Language
- Tensorflow, Pytorch - ML Framework
- Langchain/Llama Index, OpenAI Eval - Generative AI Framework
- Sentence Transformers/ Universal Sentence Encoders/OpenAI Embeddings - Vector Embeddings
- Pinecone/Doc-Array/Chroma - Vector Storage/Database
- AWS/GCP/Render - ML Apps/API Deployment
Additional
1. Data Collection
- Web Tracking: Using cookies and web beacons to track user behavior across websites. This can reveal which products or services a user is researching.
- IP Identification: Identifying and tracking the IP addresses of visitors to understand which companies are showing interest in specific topics.
- Content Syndication Networks: Gathering data from networks where companies distribute content to capture leads and gauge interest levels in specific topics.
Technologies Used:
- JavaScript trackers on web pages
- Web analytics platforms
- Content management systems (CMS) with tracking capabilities
2. Data Aggregation and Enrichment
- Data Integration: Combining data from various sources to build a complete profile.
- Data Enrichment: Adding information from external databases to enhance the understanding of each lead, such as industry data, company size, and other relevant attributes.
Technologies Used:
- Data lakes and warehouses for storing and managing data.
- ETL (Extract, Transform, Load) tools for data integration.
- Third-party data providers for enrichment.
3. Intent Signal Analysis
- ML Algorithms: To process and analyze the data, looking for patterns that signify interest or intent to purchase. These include: Logistic Regression, Decision Trees and Random Forests, Support Vector Machines (SVM), KNN, Clustering Algorithms (e.g., K-Means, Hierarchical Clustering)
- Natural Language Processing (NLP): To understand the context and sentiment behind content engagement and social media interactions.
Technologies Used:
- Machine learning frameworks like TensorFlow or PyTorch.
- NLP libraries such as NLTK or SpaCy for processing text data.
4. Predictive Modeling
- Predictive Analytics: Utilizing statistical models and machine learning to predict future behaviors based on past actions and intent signals.
Technologies Used:
- Predictive modeling tools and platforms.
- Custom-built algorithms developed by data scientists.
5. Integration and Activation
- APIs and Integrations: To feed the intent data and insights directly into CRM and marketing automation platforms for personalized marketing campaigns and sales outreach.
- Real-time Data Processing: To ensure that sales and marketing teams can act on the insights in a timely manner.
