Company Description
Senro is a real-time AI assistant built for live sales conversations. It listens to calls, understands context, and instantly surfaces accurate answers from a company’s internal knowledge base so sales reps never say “I’ll get back to you.” By helping teams handle objections and technical questions in the moment, Senro shortens sales cycles and improves close rates. The platform supports over 30 languages and dialects and is designed for global, enterprise-grade sales teams.
Role Description
This is a full-time, hybrid role based in San Francisco, CA, with flexibility for remote work. You will be the first AI Engineer at Senro, working directly with the founder. Your focus will be building and refining real-time NLP systems used during live sales calls, with an emphasis on low latency, reliability, and production readiness. This is a hands-on role with high ownership, where you will help shape both the technical architecture and the product itself.
Qualifications
- Bachelor’s degree at minimum in Computer Science, Software Engineering, or a closely related field; advanced coursework or postgraduate study is a strong plus
- Track record of building complex, production-grade NLP systems end to end, from architecture and implementation to deployment and monitoring
- Deep command of computer science fundamentals: data structures, algorithms, concurrency, networking, and systems design, with the ability to reason about tradeoffs under real-world constraints
- Extensive hands-on experience integrating large language models as real-time inference systems, including prompt design, context construction, guardrails, and failure handling
- Proven ability to design and operate low-latency, high-availability services, with clear understanding of performance bottlenecks, backpressure, and degradation strategies
- Strong software engineering rigor: clear abstractions, testability, observability, and long-term maintainability in fast-moving codebases
- Demonstrated ownership in ambiguous, zero-playbook environments, ideally as an early engineer or technical lead at a startup or high-impact product team
- Comfort making architectural decisions independently and being accountable for outcomes, not just implementation
- Strong product intuition and ability to translate real user problems into robust technical solutions
- Experience deploying, operating, and scaling systems on cloud infrastructure (AWS, GCP, or Azure) is required
