hirly

This posting is no longer listed by Parallel.

hirly last saw it live on 1 September 2026. Similar roles are on the live board.

Parallel

Early Career Research Engineer

Palo Alto

Apply through hirly

hirly scores this role against your resume, shows its reasoning, then writes a resume and cover letter for it and fills the application with you. Free to start — no card required.

hirly's read of this role

Seniority
Mid level
Work mode
Remote-friendly
First seen by hirly
1 Sept 2026

Derived automatically from the posting. Sign up to see how the role scores against your own resume.

the posting

About us

Parallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful programmatic access to the web.

We've raised $230 million from Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain to build the web for AIs. We're currently valued at $2 billion and we're forming a world-class team of engineers, designers, marketers, sellers, researchers, and operational experts to achieve our mission.

About you

You're a researcher who thinks like an engineer, or an engineer who thinks like a researcher. You've worked on information retrieval systems, embedding models, or neural ranking at scale, or you're deeply curious about the fundamental problems that emerge when training models to understand and serve billions of web documents. You thrive in the space between theory and production, where elegant solutions must also run efficiently on real infrastructure. You're comfortable reading papers from SIGIR and RecSys one day and debugging distributed training pipelines the next.

The role

You'll design and train the models that power Parallel's APIs: the intelligence layer that helps AI agents find exactly what they need from the open web. This means tackling research problems that most labs encounter only at hyperscale: How do you train embedding models that capture semantic intent across diverse query types? How do you balance model expressiveness with sub-second retrieval latency? How do you maintain index freshness when the web updates constantly, without rebuilding from scratch?

Unlike traditional search engines built for human queries, you're building for AI agents that issue complex, multi-hop queries and expect structured, programmatic responses. This is information retrieval reimagined for the LLM era, work that combines classical IR techniques with modern deep learning, applied at a scale that demands new solutions.

Life at Parallel

Our team works fully in-person , between our Palo Alto HQ and San Francisco office. We’re a flat, talent-dense organization dedicated to solving technical and creative problems.

We seek like-minded individuals who share our passion for applying science, creativity, and consistency to big and complex problems with equally big outcomes. These are our values:

Own customer impact: It’s on us to ensure real-world outcomes for our customers.

Obsess over craft: Perfect every detail because quality compounds.

Accelerate change : Ship fast, adapt faster, and move frontier ideas into production.

Create win-wins : Creatively turn trade-offs into upside.

Make high-conviction bets : Try and fail. But succeed an unfair amount.

Compensation & benefits

Competitive salary

Generous equity

Visa sponsorships

401K plans

Daily lunch & office snacks

Dinner at the office

Unlimited vacation

Caltrain pass reimbursement

Is this role actually a fit for you?

hirly answers with a score and its reasoning, then writes the resume and cover letter if you decide to go for it.

Score it against my resume
Early Career Research Engineer at Parallel — hirly