About the job
Location: New York, NY
Start date: ASAP
Languages: English (required)
About the Role
Join Pragmatike as we partner with a dynamic AI cybersecurity firm, recognized by top-tier investors and visionary AI leaders. This innovative company is dedicated to establishing robust security measures for the AI era, safeguarding enterprises from advanced threats, including deepfakes, smishing, and synthetic voice attacks.
Following a successful Series B funding round with industry-leading AI and venture partners, the organization is poised for substantial growth. Trusted by major financial institutions, technology giants, and healthcare providers, they are rapidly scaling to meet the demands of a $200 billion market opportunity.
We are seeking a Staff Machine Learning Engineer to architect and enhance the company’s machine learning capabilities from the ground up. This role is pivotal to our product vision and focuses on strategic ownership, infrastructure development, and comprehensive execution of machine learning across the organization.
Currently, there is no dedicated machine learning infrastructure or team. You will lay the groundwork: identifying where machine learning can add product value, designing end-to-end production systems, and setting the technical roadmap for the evolution of machine learning within the company.
This position is highly impactful and offers significant autonomy, ideal for candidates experienced in building production ML systems and ready to develop a robust ML function from inception to full-scale operation.
What You'll Do
Define the company's machine learning strategy: identifying application areas across products, determining necessary infrastructure, and making informed build vs. buy decisions.
Design and construct production ML systems from scratch, including data pipelines, model training workflows, evaluation frameworks, and inference serving.
Establish a rigorous evaluation methodology to assess model quality, identify regressions, and support iterative improvements based on data.
Oversee data strategy: define necessary data, structure labeling, establish feedback loops, and ensure models evolve continuously.
Collaborate closely with product and backend engineers to integrate machine learning into customer-facing systems.
Contribute production-quality code to the existing codebase and participate in architectural decision-making.
In time, assist in recruiting, mentoring, and leading the ML team as the function grows.

