Founding Machine Learning Engineer
Adaptive Security - NYC, New York, NY, United States
Posted Feb 24, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Founding Machine Learning Engineer NYC, New York, NY, United States About Adaptive NVIDIA and OpenAI's only AI cybersecurity investment. Adaptive is a cybersecurity startup on a mission to stop AI-powered cyberattacks. In December 2025, the company announced an $81M Series B led by NVIDIA and Bain Capital Ventures, with participation from Capital One Ventures, Citi Ventures, and continued support from Andreessen Horowitz (a16z), the OpenAI Startup Fund, and Abstract Ventures. The round marked NVIDIA's first AI cybersecurity investment. Adaptive was founded by Brian Long and Andrew Jones, repeat entrepreneurs who have built and scaled category-defining companies. Brian and Andrew previously co-founded Attentive, which grew to more than $500M in annual revenue and a $10B+ valuation, and TapCommerce, which was acquired by Twitter. Together, they bring deep experience building high-growth, product-led businesses at massive scale as Adaptive builds the security layer for the AI era. Trusted by leading banks, technology companies, and healthcare organizations, Adaptive protects teams from emerging threats like deepfakes, smishing, and AI-powered voice scams. With rapid enterprise adoption and a $200B+ market ahead, the company is just getting started. Role We are seeking a Founding ML Engineer to define and build Adaptive's ML capabilities. Our products use LLMs and ML models to detect, classify, and respond to cybersecurity threats in real time. The ML problems here are adversarial by nature. Attackers actively evolve to evade detection, labeled data is scarce for novel attack vectors, and models need to operate at production scale with strict latency constraints. We don't
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