Sanas is pioneering the future of human communication. Founded by a team of Stanford researchers and entrepreneurs with deep industry experience, Sanas has developed the world's first real-time speech AI platform capable of accent translation, noise cancellation, speech enhancement, cross-language communication, and more.
Sanas makes conversations clearer, more inclusive, and more effective, removing barriers that prevent people from being understood, regardless of accent, background noise, or native language.
Our team combines deep expertise in model innovation and systems engineering with a design-minded product engineering culture to build and ship cutting-edge AI models and experiences — entirely in-house.
Our innovation has been supported by the industry's leading investors, including Insight Partners, Google Ventures, Quadrille Capital, General Catalyst, Quiet Capital, and other influential investors. If you're looking to have a significant role in roadmapping and driving technical directions, if you're looking to deploy challenging and big ideas without much overhead or slowness, if you're looking to leave your mark on an ambitious, generational mission to change how the worlds thinks about speech + AI, then Sanas is a well-suited place for you.
We're looking for a Staff Software Engineer to set technical direction across multiple teams and raise the bar for how we build real-time voice AI. Define and drive architecture across our client (Desktop App / SDK), gateway and cloud backend systems.



Balance near-term customer delivery against long-term scalability as we move from contact centers into telecom and enterprise.
Own real-time performance. Treat end-to-end latency, audio quality, CPU and memory footprint and uptime as first-class requirements. Our products run live on customer calls, where regressions are immediately audible.
Take ambiguous, high-impact problems that span the SDK, backend, ML inference and DevOps teams, turn them into clear plans and see them through to production.
Write and review production code, prototype current approaches and dig into the hardest bugs across the client, backend services and deployment layers.
Establish and model best practices for code quality, testing, observability, security and operational excellence.
Coach senior and mid-level engineers and help shape how we interview and level technical talent.
Work with Product, Research/ML, Customer Success and leadership to shape the roadmap, surface technical risks early and support enterprise customer and technical requirements.
Bachelor's or Master's degree in Computer Science or a related field.

- 8-12+ years of professional software engineering experience, including significant time at a senior or staff level.




- A track record of designing and shipping large-scale, low-latency systems that served real users and held up in production.
- Deep expertise in one or more of Rust, Python & GoLang with strong command of distributed systems and RESTful API design.
- Hands-on experience with AWS, Docker and Kubernetes (preferably EKS), CI/CD and modern observability tooling.
- Familiarity with databases such as PostgreSQL and ClickHouse.
- Clear written and verbal communication, including explaining complex tradeoffs to engineers and non-technical partners.
- Sound judgment about when to build for the long term and when to ship pragmatically.

Experience with real-time audio, speech, VoIP/SIP, WebRTC, or telephony systems.
Experience productionizing ML inference on-device or in the cloud with tight latency budgets.
Experience with cross-platform desktop application or SDK development.
Experience with Kafka or other event-driven platforms and with Terraform or other infrastructure-as-code tools.
Experience scaling a system through an order-of-magnitude increase in load at a high-growth startup.
Ability to rapidly develop high-quality code with AI copilots (like Claude) using spec-driven development methods.
Contributions to open source, technical writing, or conference talks.
You understand our real-time pipeline end to end, from client and SDK through gateway to cloud. You've shipped meaningful code and identified the top technical risks and opportunities.

📌 Staff Software Engineer (ML) (Bengaluru)
🏢 Sanas
📍 Bengaluru