AI Information Retrieval Engineer (Early Career)

Parallel Web Systems; New York (NY); On-site; USD 180,000 - 260,000; 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 Web Systems in the United States (West Coast) is seeking a researcher- or engineer-turned-researcher to design and train models powering our APIs. You’ll tackle information retrieval challenges at scale, balancing expressiveness with sub-second latency, and maintaining fresh index updates as the web evolves.

You will work in a fully in-person environment with a talented team, contributing to embeddings, neural ranking, and production pipelines that serve billions of documents.

Qualifications

• Bachelor's degree or equivalent combination of education, training, and professional experience.
• A field relevant to the role as demonstrated through coursework, training, or professional experience.
• Years of experience required will correlated with the internal job level requirements for this position.

Responsibilities

• Design and train models powering Parallel's APIs for AI agents.
• Address information retrieval challenges at hyperscale with low-latency requirements.
• Bridge classic IR techniques with modern deep learning in production systems.

Skills

Bachelor's degree or equivalent

Field relevant to the role

Experience level tied to job level

Education

Bachelor's degree or equivalent

Tools

None

Job description

Parallel Web Systems in the United States (West Coast) is seeking a researcher- or engineer-turned-researcher to design and train models powering our APIs. You’ll tackle information retrieval challenges at scale, balancing expressiveness with sub-second latency, and maintaining fresh index updates as the web evolves.

You will work in a fully in-person environment with a talented team, contributing to embeddings, neural ranking, and production pipelines that serve billions of documents.