---
title: 'Senior SDET at Smallest'
canonical: 'https://feeny.ai/job/senior-sdet-smallest-bengaluru-wctsdg44ykbc'
type: 'job'
last_seen: '2026-10-16'
---

# Senior SDET at Smallest 

- **Company:** Smallest 
- **Location:** Bengaluru, India
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-05
- **Last confirmed live:** 2026-10-16
- **Apply:** https://jobs.ashbyhq.com/smallest/d251ba4a-8ae6-4e3e-b918-1c77e4555a68

## Job description

Senior QA Automation & Performance Engineer Location: Bengaluru Employment Type: Full-time, On-site

About [smallest.ai](http://smallest.ai) [smallest.ai](http://smallest.ai) is building next-generation AI infrastructure and voice technology to power intelligent products at scale. We work across speech, LLMs and real-time AI systems, building products and infrastructure that developers and businesses rely on. We are looking for a hands-on Senior QA Automation & Performance Engineer to own quality engineering across our backend systems. You will design and build automation frameworks, perform functional and performance testing, and help ensure that our APIs and services remain reliable as traffic, customers and system complexity grow. This is an individual-contributor role requiring strong technical ownership and close collaboration with engineering, product and infrastructure teams.

## What You’ll Do

- Own functional, integration, end-to-end and performance testing for backend services and APIs.
- Design, build and maintain scalable automation frameworks rather than only adding test cases to an existing framework.
- Automate testing across a multi-service, multi-tenant platform.
- Validate tenant isolation, authentication, permissions, configuration differences, data integrity and concurrent tenant activity.
- Design and execute load, stress, spike, scalability and endurance tests.
- Define performance benchmarks and acceptance criteria for latency, throughput, concurrency, reliability and error rates.
- Identify performance bottlenecks and work with backend and infrastructure engineers to resolve them.
- Test business logic, data flows, asynchronous processes, integrations and complete customer workflows.
- Build reusable test utilities, fixtures, test-data strategies and environment-management capabilities.
- Integrate functional and performance test suites into CI/CD pipelines.
- Analyse failures using logs, metrics and traces, and distinguish product defects from environment or test-framework issues.
- Identify edge cases, regressions, failure scenarios and production risks before release.
- Improve test coverage, quality gates and overall QA processes across the engineering organisation.
- Collaborate effectively with engineers while independently driving testing priorities and execution.

## What We’re Looking For

- 4+ years of experience in software quality engineering, test automation or SDET roles.
- Strong hands-on experience with both functional testing and performance testing.
- Demonstrated experience building or significantly extending an automation framework from the ground up.
- Experience testing backend APIs, distributed services and end-to-end workflows.
- Experience working with multi-service architectures; exposure to multi-tenant SaaS systems is strongly preferred.
- Hands-on experience with performance-testing tools such as k6, JMeter, Gatling or Locust.
- Ability to design realistic workload models and analyse latency percentiles, throughput, concurrency, saturation and failure rates.
- Strong programming or scripting skills in at least one language such as Python, Java, JavaScript/TypeScript or Go.
- Experience integrating automated tests into CI/CD systems.
- Working knowledge of databases, queues, caching, APIs and asynchronous workflows.
- Familiarity with logs, monitoring and observability tools for troubleshooting test failures and performance bottlenecks.
- Strong understanding of testing fundamentals, SDLC, STLC and defect-management practices.
- Ability to operate as an independent contributor while working closely with multiple engineering teams.
- Strong analytical thinking, attention to detail and clear written and verbal communication.

Good to Have

- Experience in a high-growth or product-led technology startup.
- Experience testing real-time, AI, voice, streaming or other latency-sensitive systems.
- Familiarity with Kubernetes, Docker and cloud infrastructure.
- Experience testing WebSockets, streaming APIs or event-driven architectures.
- Experience with security, resilience, failover or chaos testing.
- Experience validating SLA/SLO requirements and production performance metrics.

Why Join [smallest.ai](http://smallest.ai)

- Build the testing and automation foundation for cutting-edge AI products.
- Work on real-time, large-scale and latency-sensitive systems.
- Own quality engineering across a complex, multi-service platform.
- Work in a high-ownership environment with direct engineering impact.
- Help the organisation ship faster while maintaining reliability and performance.

## About Smallest 

## Company Overview
- **One-liner**: Smallest.ai builds compact, high-performance voice and multi-modal AI models for real-time voice agent orchestration.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Akshat Mandloi (Co-Founder); other founders not publicly disclosed.

## Core Business
- **Primary industry/industries**: Voice AI, Artificial Intelligence, Multi-modal AI, Enterprise AI Infrastructure.
- **Target customers**: B2B – enterprises, developers, and platform teams building voice agents for healthcare, debt collection, real estate, e-commerce, and small businesses.
- **Mission or purpose statement**: “Driving the future of small, efficient multi-modal models.” The company believes AI will evolve like human intelligence – many small specialized models rather than one massive model.

## Products & Services
- **Text-to-Speech (TTS) – Lightning**: Blazing-fast TTS for real-time voice agents with 100ms latency, supporting 15+ languages.
- **Speech-to-Text (STT) – Pulse**: High-accuracy transcription in 38 languages with emotion and speaker detection.
- **Speech-to-Speech (S2S)**: One of the first native speech-to-speech models built for production.
- **Small Language Model (SLM)**: A sub-3B parameter language model that outperforms GPT-4.1 on key benchmarks.
- **Voice Cloning**: Real-time voice cloning capability integrated into the platform.
- **Agent Platform – Hydra**: A unified interface to configure voice agents, select voices, set languages, and go live. Supports on‑prem and private cloud deployments.
- **Enterprise Compliance**: SOC 2 Type II, GDPR, HIPAA, and PCI compliant.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company).
- **Key Metric – Total Funding**: $9 million (Seed round $8M led by Sierra Ventures in Sept 2025; Pre-Seed and Non-Equity Assistance from AWS).
- **Notable Investors/Partners**: Sierra Ventures (lead), Amazon Web Services (AWS), Ashish Kakran (Seed investor and board member).
- **Growth Signals**:
  - 64 employees (as of mid‑2026); 3.1% monthly headcount growth.
  - 32,960 LinkedIn followers; active hiring with 4 open positions.
  - Presence in the US, India, and Canada; 46% of workforce in India, 27% in the US.
  - SOC 2/GDPR/HIPAA/PCI compliance enables adoption in regulated industries.

## Competitive Advantages
- **Efficiency vs. Size**: Sub-3B SLM outperforms GPT-4.1, enabling lower cost and faster inference without sacrificing quality.
- **Ultra‑Low Latency**: Real-time voice with 100ms end‑to‑end latency for TTS.
- **Multi‑Modal Native**: First‑to‑market native speech‑to‑speech model alongside SLM, STT, and TTS.
- **Enterprise‑Grade Security**: On‑premise and private cloud deployment with full compliance certifications (SOC 2, HIPAA, GDPR, PCI).
- **Single‑Platform Orchestration**: Hydra agent platform unifies model selection, voice config, language, and deployment.

## Strategic Focus
- Scale adoption of voice AI across regulated verticals (healthcare, financial services, debt collection).
- Continue R&D on smaller, more efficient models that can run on‑premise.
- Grow enterprise sales and expand partnerships (e.g., AWS).
- Build out the agent orchestration layer to reduce integration friction for developers.

## Why Work Here
- **Engineering Culture**: Small research lab with a focus on bleeding‑edge model efficiency; strong emphasis on shipping production‑grade systems.
- **Remote/Hybrid Policy**: Not explicitly stated; offices in San Francisco, India, and Canada, suggesting a hybrid or flexible approach.
- **Notable Perks**: Opportunity to work on frontier AI models from scratch, influence product direction, and deploy at scale. Access to enterprise‑grade infrastructure and compliance frameworks.
- **Team Composition**: 20% technical, 9% research, with founders deeply involved; talent sourced from top tech firms (Qualcomm, Meesho, Observe.AI).

## Sources
1. [smallest.ai](https://smallest.ai/)
2. [LinkedIn – smallest.ai](https://www.linkedin.com/company/smallest)
3. [Jobs.ashbyhq.com – Smallest](https://jobs.ashbyhq.com/smallest)
4. [PitchBook – smallest.ai profile](https://pitchbook.com/profiles/company/596572-75)
5. [Tracxn – smallest.ai profile](https://tracxn.com/d/companies/smallestai/__M_bkNG86V8a7CnAiGLw2oCkEQHuMuRhd0YAMsTbEeGU)

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