Who is Recruiting from Scratch: Recruiting from Scratch is a talent firm that focuses on placing the best candidate for our clients. Our team is 100% remote and we work with teams across North America, South America, and Europe to help them hire.
Our client is hiring a Senior Product Engineer to join their team. Company Size: 45
This role is hybrid in San Francisco. The Role: Concurrency & distributed systems - Our smart dialer places calls in parallel and runs a realtime AI model on each call. There are some interesting concurrency problems syncing state between Twilio, our backend, and the frontend, and knowing which calls to connect, which to continue in the background, and when to start the next call. Realtime audio AI & precision/recall/latency tradeoffs (algorithms & models) - We use audio data, transcription, silence detection, and several other signals to detect whether a live phone call is a voicemail, a human, or a dial tree. Here, latency is a third factor added to the standard precision/recall tradeoff because it's important we can detect humans quickly. Our approach involves LLM embeddings, few-shot learning, data labeling, and continuous monitoring of model performance in prod. Latency (infrastructure) - If our model took 5 seconds to detect a human on a phone call, the human would hang up. It's imperative we can detect quickly and that our users can execute calls quickly. There's latency across the detection pipeline including transcription models, audio models, websockets, Twilio API, database transactions, etc. Smart call funnels & playbooks (data wrangling, backend eng, GPT-3, UX) - At what point in the conversation do my reps get stuck? What are the toughest questions that we need to address? Can I "program" a playbook so that the product will help my team standardize toward best-practices? We're using GPT-3 and other LLM's to turn companies' mostly unstructured call data into actionable strategies & feedback loops. Conversation embeddings & markov models (ML modeling) - What does the anatomy of a call look like? If I say XYZ, what are the different ways the prospect might answer and the probabilities of each? Conditioned on the first half of the call, what do I say next to maximize the likelihood that I book a demo at the end of the call? Can we use LLM's to generate embeddings of conversations that we can use to cluster similar conversation patterns and predict where the conversation is headed? Tech stack Frontend: React, Typescript, MobX, Backend: Node.js, Express, Typescript, Technologies: Firebase, Firestore, Websockets, Twilio, WebRTC, Postgres, Redis, ML: GPT, Transformers, PyTorch, signal processing, few-shot classification.
Candidate Requirements: 3+ years experience building complex systems (ideally somewhat related to ours) You're a confident, independent, and experienced engineer who is used to extreme ownership and solving hard problems 5+ years of experience as a software engineer or in a related technical role Compensation: $160,000 - $240,000 base + equity
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