Sift

Sift

AI-driven real-time fraud prevention for e-commerce

Contracts and Legal Operations Specialist

Full-Time
$90k - $125k/yr
Junior
Remote in USA+2 more

More locations: Seattle, WA, USA | San Francisco, CA, USA

Remote

About the job

Requirements
  • At least 2 years of experience in a contracts, legal operations, or deal desk role, ideally at a business-to-business software-as-a-service company.
  • No law degree is required.
  • Comfort using artificial intelligence tools daily to accelerate drafting and review.
  • Good judgment in distinguishing standard matters from those requiring escalation to legal counsel.
  • Experience with Salesforce and Jira.
  • Strong organizational and time-management skills, with the flexibility to handle volume and competing requests.
  • Comfort working in a role where processes and tooling continue to change.
Responsibilities
  • Draft, negotiate, and close standard commercial agreements independently, including non-disclosure agreements, order forms, standard renewals, and low-risk vendor and partner agreements, from first draft through signature.
  • Review and finalize artificial-intelligence-generated first-pass redlines and clause extractions on incoming contracts before sending them to counterparties.
  • Own contract automation and playbooks by updating clause libraries, adjusting intake logic, and adding agreement types as the business changes.
  • Manage vendor and procurement requests end to end, including negotiating standard pricing and terms, tracking requests through Jira, and rejecting terms that do not work for Sift.
  • Manage the full contract lifecycle, including intake, repository management, renewals, approvals, and terminations.
  • Coach Sales on standard terms and redlines so routine deals can close without legal review of every file.
  • Collaborate with Sales and Deal Desk to streamline the sales process and related deal cycles.
  • Track and report on contract and vendor metrics.
  • Triage inbound contracts and vendor requests, escalating complex, high-risk, or non-standard matters to legal counsel.
  • Perform other duties as part of a small legal and compliance team.
Desired Qualifications
  • Prior legal, paralegal, or compliance experience.
  • Experience with a contract lifecycle management platform such as DocuSign Navigator, Ironclad, LinkSquares, or Conga.
  • Exposure to data privacy or data processing agreement concepts, including CPRA and GDPR.
  • Experience in a fraud, risk, or trust and safety adjacent business.
  • Hands-on experience using large language model tools such as Claude or ChatGPT to build custom prompts, workflows, or lightweight automations.
  • Prior experience working in a high-growth company.

About the company

Sift builds a Digital Trust & Safety platform that helps e-commerce and fintech companies prevent fraud while keeping legitimate customers moving. It uses machine learning and AI to analyze vast streams of data in real time, delivering risk scores and decisions that integrate with clients’ existing systems. The platform provides dynamic friction—allowing valid transactions to proceed smoothly while stopping fraudulent ones—and it scales across small online retailers to large financial institutions. Revenue comes from a subscription model plus consulting and custom integration services. Sift’s goal is to reduce chargebacks and account takeovers, protect customer trust, and support safe digital commerce at scale, by delivering precise, real-time fraud prevention without disrupting normal user experiences.

Company Size

201-500

Company Stage

Series E

Total Funding

$156.7M

Headquarters

San Francisco, California

Founded

2011

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Simplify's Take

What believers are saying

  • Sift reported account-takeover rates down 28% year over year in 2026.
  • The company’s March 2026 and August 2026 releases expanded investigation tooling and identity trust.
  • May 13, 2026 leadership hires from Meta, Anduril, SpaceX, and Palantir strengthen execution.

What critics are saying

  • Sift’s own 2026 data shows block rates falling from 3.29% to 2.82%.
  • Coordinated fraud rings across 90+ businesses defeat isolated controls and raise false-positive pressure.
  • If Stripe, Adyen, or platform-native tools bundle comparable fraud controls, Sift loses relevance fast.

What makes Sift unique

  • Sift’s Global Data Network spans 700+ businesses and 34,000 sites and apps.
  • Its fraud models link devices, accounts, and payments across merchants, not single sessions.
  • Sift held #1 G2 rankings across fraud prevention categories in Fall 2025.

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Benefits

Flexible Work Hours

Growth & Insights and Company News

Headcount

6 month growth

↓ -4%

1 year growth

↓ -3%

2 year growth

↓ -4%
SpaceNews
Jun 25th, 2024
Sift raises $17.5M in Series A

Southern California startup Sift raised $17.5 million in a Series A funding round led by Google Ventures. Announced on June 25, the investment will help Sift expand its staff and accelerate the development of its platform, which aids engineers in analyzing hardware sensor data. Sift's automated data review product generates reports to highlight potential issues, benefiting industries like aviation, defense, energy, and transportation.

Globe Newswire
Nov 17th, 2021
Sift Acquires Passwordless Authentication Pioneer Keyless to Provide Regulated Businesses and Online Merchants with Secure, Frictionless Authentication

Biometric authentication innovator eliminates password-based account takeover and enables PSD2 Strong Customer Authentication while preserving user privacy

TechCrunch
Apr 22nd, 2021
Fraud prevention platform Sift raises $50M at over $1B valuation, eyes acquisitions

With the increase of digital transacting over the past year, cybercriminals have been having a field day.

PYMNTS
Oct 8th, 2020
The Ordering Innovations Attracting Restaurant Customers

This Deep Dive explores how restaurants are using innovative dining and ordering technology to satisfy customer needs amid the COVID-19 pandemic.

PYMNTS
Sep 22nd, 2020
Deep Dive: How QSRs Can Fight The Rise Of App-Enabled Friendly Fraud

This Deep Dive examines how the increased use of mobile apps has led to a rise in friendly fraud and how AI and machine learning can help fight it.