Early Career Research Engineer

Parallel; Palo Alto (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Generous equity

Visa sponsorships

401K plans

Daily lunch & office snacks

Dinner at the office

Unlimited vacation

Caltrain pass reimbursement

Job summary

Parallel in Palo Alto is seeking a researcher or engineer to design and train models that empower our APIs for AI agents. This role involves tackling significant information retrieval challenges with a focus on integrating classical techniques and modern deep learning.

Our ideal candidate thrives at the intersection of theory and practical application, eager to work fully in-person while contributing to a collaborative environment that values high-impact customer outcomes. Competitive salary and generous equity are among the benefits offered.

Qualifications

• Experience in designing and training models for AI agents.
• Proficiency in information retrieval and deep learning techniques.
• Ability to handle complex research problems and production constraints.

Responsibilities

• Design models powering Parallel's APIs for AI agents.
• Tackle hyperscale research problems in information retrieval.
• Balance model expressiveness with retrieval latency.

Skills

Information retrieval systems

Embedding models

Neural ranking

Deep learning

Distributed training pipelines

Job description

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