Member of Technical Staff, Search Ranking

Parallel Web Systems; California; On-site; USD 170,000 - 250,000; Full time

Benefits offered by this job

Competitive salary

Generous equity

Visa sponsorships

401K plans

Lunch & office snacks

Dinner at the office

Unlimited vacation

Caltrain pass

Job summary

Parallel Web Systems is seeking a skilled engineer to own the multi-stage ranking pipeline that narrows a web-scale set of passages for queries, optimizing compute and accuracy. You will push precision and recall across fast candidate retrieval and heavier neural reranking while balancing latency and cost.

The position is fully in-person across our Palo Alto HQ and San Francisco office, with a culture focused on craft, impact, and collaboration.

Qualifications

• Experience with multi-stage ranking pipelines for large-scale data.
• Interest in balancing quality, latency, and cost in production systems.

Responsibilities

• Own the multi-stage ranking pipeline that narrows web-scale candidates to top passages.
• Tune retrieval, ranking, and neural reranking for real-time use cases.
• Balance compute cost with accuracy and system latency in production.

Skills

Ranking pipeline

Relevance modeling

ML systems

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.

Job: You will own the multi-stage ranking pipeline that narrows a web-scale set of candidates down to the handful of best passages for a query, spending the least compute necessary to do it well. You will push precision and recall across fast candidate retrieval, lightweight first-pass ranking, and heavier neural reranking, decide how much compute each query deserves, and grade everything on real usage outcomes rather than offline proxies.

You: Have deep intuition for relevance, feature engineering, and the trade-offs between quality, latency, and cost. Think rigorously about how ranking, retrieval, and agent behavior inform one another, and how to train models that serve all three. You care about your research being applied to product and systems that millions use.

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