SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Adaptive Security is an AI cybersecurity startup backed by NVIDIA, OpenAI, and other top-tier investors ($146M raised, including $81M Series B). The company detects and responds to AI-powered cyberattacks—including deepfakes, smishing, and voice scams—for leading banks, tech companies, and healthcare organizations.
You will be Adaptive's 2nd ML Engineer and will define and build the company's ML capabilities from the ground up. The role is foundational: there is no dedicated ML infrastructure or team today. You'll set technical direction, stand up infrastructure, and execute hands-on work. As the ML surface area expands, you'll grow and lead the function.
Key responsibilities:
- Define Adaptive's ML strategy: where ML applies across products, what infrastructure is needed, and build vs. buy decisions.
- Design and build production ML systems end-to-end: data pipelines, model training, evaluation frameworks, and inference serving.
- Establish evaluation methodology to measure model quality, catch regressions, and drive data-informed decisions.
- Own the data strategy: labeling approach, feedback loops, and continuous model improvement.
- Partner with product engineers to integrate ML into the product; write production code within the existing codebase.
- Build and lead the ML team as scope grows.
The ML problems are adversarial in nature—attackers actively evolve to evade detection, labeled data is scarce for novel attack vectors, and models must operate at production scale with strict latency constraints.
QUALIFICATIONS:
- 4+ years of experience building ML systems in production, ideally with experience standing up the ML function at an early-stage startup or as the senior/lead ML person at a previous company.
- Strong software engineering fundamentals: production-quality code in Python, Java, or TypeScript; experience working within large codebases.
- Experience with cloud ML infrastructure (AWS SageMaker, Bedrock, Modal, Baseten, or similar).
- Experience with ML and data processing frameworks (PyTorch, TensorFlow, Spark).
- Comfortable working across the stack: infrastructure, backend services, and data systems.
- High autonomy; ability to define the path forward and drive execution with leadership support.
