---
title: 'Machine Learning Intern at Bland'
canonical: 'https://feeny.ai/job/machine-learning-intern-bland-san-francisco-kkfd0gny0xrn'
type: 'job'
last_seen: '2026-09-10'
---

# Machine Learning Intern at Bland

- **Company:** Bland
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-28
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/bland/c8a5c0de-935d-4f76-bc7d-237cbfb2cf55

## Job description

The Role: Machine Learning Research Intern, Audio As a Research Intern at Bland, you will own a focused research project across our voice stack: speech-to-text, large language models, neural audio codecs, or text-to-speech. You will work alongside our research team on the same problems they are working on, not on a side track built to keep interns busy. We scope internships around a single meaningful question that can be answered in the time you have. The goal is a result worth shipping, publishing, or both. Interns here regularly see their work reach production systems handling millions of calls.

## What You Will Do

Own a research question end to end

- Take one well-scoped problem from literature review through implementation, experimentation, and results.
- Design ablations that isolate what actually caused an improvement.
- Present your findings to the research team and defend the methodology.

Work on real systems

- Train and evaluate models on large-scale, real-world telephony audio, including the accents, noise, and artifacts that make production speech hard.
- Use our distributed GPU infrastructure rather than toy-scale setups.
- Where the result warrants it, work with engineers to move it toward production.

Choose your depth Depending on your background and interests, your project may focus on:

- Expressive and controllable text-to-speech, including prosody and emotion modeling
- Neural audio codecs and discrete or continuous speech representations
- ASR robustness for telephony, accents, and code switching
- Real-time and streaming inference under latency constraints
- Full-duplex conversation and turn-taking dynamics

What Makes You a Great Fit Research foundations

- Currently pursuing a MS or PhD in ML, CS, EE, or a related field, or equivalent research experience.
- Comfortable reading a paper and reimplementing it without hand-holding.
- Experience with self-supervised, generative, or multimodal modeling.

Audio or speech grounding

- Hands-on work with speech or audio models, whether TTS, ASR, codecs, or audio representation learning.
- Strong intuition for audio quality and what makes synthetic speech sound wrong.
- Prior publications or open source contributions in speech or language AI are a strong signal, though not required.

Engineering ability

- Fluent in PyTorch and comfortable in a real codebase.
- Able to run your own experiments on GPU clusters without waiting to be unblocked.

## How You Show Up

- You identify the single experiment that validates an idea in days, not months.
- You measure everything and let data drive decisions.
- You are honest about negative results, because they are how we narrow the search.
- You are obsessed with making voice agents sound truly human.
- You use AI tools aggressively to amplify your own impact.

## Benefits

- Competitive intern compensation
- Mentorship from researchers working on frontier voice AI
- Every tool you need to succeed
- Beautiful office in Levi's Plaza, SF with rooftop views
- A real shot at a return offer

## About Bland

## Company Overview
- **One-liner**: Bland provides an enterprise voice AI platform for building, deploying, and monitoring AI phone agents that hold natural conversations at scale.
- **Entity Type**: Private (Series C, over $100M raised)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2023
- **Founders**: Isaiah Granet and a second co-founder (Y Combinator Summer 2023 batch)

## Core Business
- **Primary industry**: AI voice agents / enterprise AI infrastructure
- **Target customers**: Large enterprises, regulated industries (healthcare, finance, insurance, etc.) – B2B
- **Mission**: To automate phone calls that previously required a human, with security and trust built from the ground up.

## Products & Services
- **Bland Voice AI Platform (SaaS / On‑premises)**: A full‑stack platform combining custom speech‑to‑text, large language model (LLM), text‑to‑speech, and telephony. Supports inbound and outbound calls across voice, SMS, iMessage, and web chat. Unified memory across channels and real‑time observability.
- **Conversational Pathways**: A proprietary programming language that enables users to create branching, deterministic call flows to reduce hallucinations.
- **Forward Deployed Engineering**: Professional services that build the first production agent end‑to‑end in 2–6 weeks, integrating with the customer’s CRM, telephony, and scheduling tools.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed (latest round: Series C)
- **Key Metric (Funding)**: Over $100M total raised (Series C announced in 2025)
- **Notable Investors/Partners**: Y Combinator (S23), Team Ignite Ventures, Max Levchin (PayPal co‑founder), Jeff Lawson (Twilio co‑founder)
- **Growth Signals**: 522M+ calls resolved to date, 47M+ calls per month, ~100 employees, $2M ARR achieved within four months of the initial pivot. Certifications include SOC 2 Type II, HIPAA, PCI DSS v4.0, and GDPR – enabling adoption in highly regulated verticals.

## Competitive Advantages
- **Full‑stack ownership**: Bland built its own voice models, LLM, text‑to‑speech, and speech‑to‑text – not a wrapper around third‑party APIs. This provides lower latency (~400ms) and full control over model behavior.
- **Compliance‑first design**: SOC 2, HIPAA, PCI DSS, and GDPR out of the box. Self‑hosted and on‑premises deployments available for sensitive workloads.
- **Proprietary orchestration**: The Conversational Pathways language allows deterministic, auditable call flows that reduce hallucination risk.
- **Rapid deployment**: Typical go‑live in 30 days with dedicated Forward Deployed Engineer support.

## Strategic Focus
- **Regulated industries**: Deepening traction in healthcare, financial services, and insurance where compliance and data residency are critical.
- **Global expansion**: Multi‐language support (40+ languages, real‑time translation in 23) and data residency options in US, EU, and APAC.
- **Channel expansion**: Unifying agent memory across voice and messaging to offer a single omnichannel AI assistant.

## Why Work Here
- **Culture**: “We hire for obsession, not pedigree.” Emphasis on work ethic, accountability, and direct ownership. Flat structure with minimal approval chains (“No committees. No approval chains. Ship the right thing, measure the result, iterate.”)
- **Engineering focus**: The company built its own models and infrastructure – ideal for engineers who want to work on frontier AI problems rather than stitching together APIs.
- **Location & flexibility**: Based in San Francisco with remote (US) options for some roles. The office environment is startup‑intense; the co‑founder famously slept on a closet floor during the early pivot.
- **Notable perks**: Not explicitly listed, but the company highlights a “build it yourself” ethos, early‑stage equity, and direct impact on a product that processes tens of millions of calls per month.

## Sources
1. [bland.com](https://www.bland.com/)
2. [bland.ai](https://www.bland.ai/about)
3. [ycombinator.com](https://www.ycombinator.com/companies/bland-ai)
4. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/bland)

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