Internationalization NLP Lead

The Opportunity

Our Detection team powers our core ML/AI capabilities we leverage to detect and mitigate attacks. You’ll find your work on our Machine Learning team to be at the very center of Abnormal’s success - giving your work incredible impact on our business and customers alike!

As Abnormal expands to foregin markets, we are looking for a tech lead that owns and drives the machine learning strategy of internationalization expansion for message detection products. The key challenge here is not only extracting text signals from each foreign language for model building but also leading toward a language agnostic modeling architecture to ensure scalable and repeatable successes in near future.

You’ll find our team passionate about building a detection engine unparalleled in its ability to stop every attack - accurately and timely. Our customers put their trust in Abnormal and we take that seriously. Our team’s culture of excellence means we analyze misclassification and hypothesize solutions to constantly create new features, models, and systems that help us combat whatever evil our (and our customer’s) adversaries throw at us.

Who you are

• Solid NLP engineer with good coding skills
• Experience in designing scalable modeling solution to expand to foreign languages, especially CJK languages
• Cross functional leadership for roadmap planning and execution
• Excellent communication and organizational skills
• Strong project management and ability to work backwards from product requirements
• Passionate about the mission to stop email based attacks and eager to make impact

What you’ll do

• Collaborate with stakeholders and co-own roadmap for German, French, Japanese, etc. product expansion.
• Work with engineers to implement necessary architecture changes for phrase lists, domain db, brand db
• Collaborate with language specialist to add in foeign phrases, domains, brands
• Define text processing strategy for foreign regions with Asian/CJK languages
• Define and implement model architecture that is scalable for a new foreign language
• Lead model training for a foreign language with early design partners and quickly iterate hypotheses to optimize detection efficacy
• Influence / collaborate with content understanding team with internationalization requirements

Experience you’ll need

• Your degree (or degrees) in Computer Science
• Deep Learning, Natural Language Processing (NLP/NLU), Entity Extraction
• Experience in building ML product for foreign languages, especially CJK languages
• At least 4 Years of end-to-end, production Machine Learning experience (we ship our own code)
• Strong analytical & data science skills
• Comfortable working in Python
  

Plus experience in one (or more) of the following:

• PhD in NLP domain, especially BERT expert
• Security (or related) domain: Fraud, Abuse, Impersonation, Trust, Spam, Phishing, Privacy, or similar
• E2E MLE experience with rapid-growth/pre-IPO, customer-centric, SaaS startups

More About Abnormal Security

Abnormal Security is defining the next generation of email security defense. Our platform uses machine learning and artificial intelligence to baseline communication content, user identity, and behavioral signals in real-time and at-scale in order to detect the abnormalities of email attacks. Customers love us because we consistently detect and stop what everyone else in the market can’t -- advanced attacks that have never been seen before -- and we do so with beautiful user interfaces and best-in-industry customer support.

Our veteran team has built some of the most enduring machine learning platforms at leading companies including Google, Twitter, Pinterest, Amazon, Microsoft, and Expanse. We are located in San Francisco,CA, New York, NY and Lehi, UT.

Our company is growing - we’re on the Forbes AI 50, selected as a Gartner 2020 Cool Vendor, and our customer base includes multiple Fortune 500 companies.

Abnormal Security is committed to creating a diverse work environment. All qualified applicants will receive consideration without regard to race, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status