Recruiting from Scratch

We work with companies funded by the best investors including Sequoia Capital, Lightspeed Ventures, Tiger Global Management, A16Z, Accel, DFJ, and more.. 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.. DB - Postgres with Prisma ORM, Redis. Natural Language Processing (NLP): To understand the context and sentiment behind content engagement and social media interactions.. Machine learning frameworks like TensorFlow or PyTorch.

engineer

Who is Recruiting from Scratch :
Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire. We work with companies funded by the best investors including Sequoia Capital, Lightspeed Ventures, Tiger Global Management, A16Z, Accel, DFJ, and more.
Our client recently raised a Seed round and looking to expand aggressively. They a utomate away roboticism, monotony, and repetition in professional life through the use of artificial intelligence. Creating more time in the world for humans to perform human actions in the workplace.
TL/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.
Salary Range: $100,000-$140,000 base.
https://www.recruitingfromscratch.com/

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