Founding AI/ML Engineer - Bangalore.

Were building an AI system to accelerate teamwork for software teams.

Most AI tools accelerate individuals. We want to accelerate teams.

Starting by building systems that understand what teams are talking about, what needs to
get done & bringing in contextual agents to move the work forward with minimal human
effort.

Were hiring a founding AI/ML engineer to own the intelligence layer of the product.

The work youll do :
The role spans practical, real-world AI/ML where correctness, latency & reliability are
critical.

- Cross-Channel Communication Understanding : Build systems that understand and classify content across Email, Slack, issue trackers, and documents into tasks, status checks, meeting requests, information, or noise.

- Task Domain Identification : Determine the domain of each task (engineering, design, product, writing, support, etc.) based on message content + context.

- Work Decomposition - Infer the actual work needed : Take a vague task request and convert it into structured, actionable work units.

- Agent Routing & Orchestration : Once the work is known, trigger internal or external agents that can help complete it, with human confirmation where required.

- Project/Topic Mapping : Cluster related messages across channels into implicit Projects without teams needing to create them manually.

- Autonomous fetch of latest Status : For messages asking Where does this stand?, pull data from Slack threads, Jira/Linear, Git/ GitHub/Bitbucket, and prepare an accurate update for user approval.

- Scheduling Intelligence : Detect meeting-related asks, identify participants, find availability, and propose times.

Youll work on problems that combine :

- LLM reasoning in messy real-world environments

- RAG across live organizational systems

- Dynamic agent orchestration

- High-bar product reliability

- User-in-the-loop workflows

- Execution-critical latency constraints

Youll be building the core intelligence of the product, not supporting it.

Who will thrive :

- Youve built and shipped ML/LLM systems in production.

- You can own data modeling evaluation infra deployment.

- You think in terms of reliability, precision/recall, and trust.

- You prefer small teams and fast iterations.

- You want to build something the best global teams rely on every single day.