Staff machine learning engineer (L4) at Twilio, remote across India, building new ML functionality for a fast-moving product.

This remote-first India role was posted on 8 October 2026. The listing describes building new functionality within a fast-moving product seeing rapid adoption, and taking a nebulous idea, designing a rigorous experiment, and executing it within weeks. Twilio's listing emphasizes remote-first work and encourages candidates from all kinds of industries to apply.

About Twilio

Twilio builds communications APIs - messaging, voice, email and customer engagement - used by hundreds of thousands of businesses, per its listings. The company describes itself as remote-first.

Twilio hires remotely across India for engineering roles, including machine learning positions, per its listings. Learn more on Twilio's website.

The role in plain terms

The experiment framing - nebulous idea to rigorous experiment in weeks - defines the tempo: this is applied ML with research flavor, not a long-horizon research lab. The staff (L4) level implies owning technically ambiguous work without close direction. The sibling L5 principal posting on the same product suggests a small senior team, where staff engineers carry real scope.

The team and the work

Per the listing, the product area involves applied ML in Twilio's communications and customer-engagement platform, working remotely from India with global teams. The listing's required-qualifications section is worth reading in full on the employer's page before applying.

What the employer asks for

These requirements are quoted from the employer's listing, kept in their exact words so eligibility details are not distorted:

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just...
8+ years applied ML/AI experience with a track record of owning technically ambitious systems end to end rather than contributing to someone else's design.
Comfort operating in a 0-to-1 setting with ambiguous requirements and a strong bias for action.
Proficiency in Python programming language.
Experience building cloud-based services on AWS, GCP, or Azure, with high-volume data, streaming or real-time inference paths, and a range of data stores.
Strong fundamentals of statistical machine learning algorithms, transformer models, LLMs
Ability to raise the technical bar of a team through mentorship, design review, and standards, without formal management authority.
Experience in building cloud-based services using AWS, GCP, or Azure.

Who this role suits

Applied ML engineers operating at staff level who like fast experiment cycles and production impact. Not for ML engineers who prefer well-specified tasks or pure model-training work.

Practical notes

For staff applied-ML roles, expect to walk through an ambiguous problem you turned into a shipped experiment: hypothesis, design, result, decision. Remote-first companies typically also assess written communication.

Working at Twilio

Engineering at Twilio spans communications infrastructure, customer engagement products and applied machine learning for personalisation at scale.

How to apply

Apply directly on the employer's site: Staff Machine Learning Engineer (L4) at Twilio. We found this listing on the Greenhouse public job board (job-boards.greenhouse.io), where it was posted on 8 Oct 2026 and was still open when we checked on 30 Oct 2026. Before applying, re-read the employer's own description in full, since eligibility details, deadlines and process steps can change at any time and the employer's page is always the final word. Tailor your resume to the specific requirements above rather than sending a generic one: mirror the skills and tools the listing names where you genuinely have them. A short, specific note on why this team and this problem interest you goes further than a long generic cover letter. Never pay a fee to apply or interview; genuine employers, including this one, do not charge candidates.

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This is our short summary of the employer's listing. The full description, eligibility and deadline are on the employer's page.