Founding Machine Learning Engineer

Location:

New York, NY

Start date:

ASAP

Languages:

English (required)

Pragmatike is hiring on behalf of a high-growth AI cybersecurity company backed by top-tier investors and strategic AI leaders. The company is building the security layer for the AI era, protecting enterprises against AI-powered threats such as deepfakes, smishing, and synthetic voice attacks.

Following a major Series B funding round led by industry-leading AI and venture partners, the company is entering a critical growth phase. Trusted by major banks, technology companies, and healthcare organizations, they are scaling rapidly to meet enterprise demand in a $200B+ market opportunity.

We are looking for a Staff Machine Learning Engineer to define and build the companys ML capabilities from the ground up. ML is central to the product vision. This role is not about incremental optimization — it is about owning the strategy, infrastructure, and execution of machine learning across the organization.

There is currently no dedicated ML infrastructure or ML team. You will establish the foundations: define where ML drives product value, design production systems end-to-end, and set the technical direction for how ML evolves across the company.

This is a high-impact, highly autonomous role suited for someone who has built ML systems in production and is ready to architect an ML function from zero to scale.

• Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions.

• Design and build production ML systems end-to-end — including data pipelines, model training workflows, evaluation frameworks, and inference serving.

• Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration.

• Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve.

• Partner closely with product and backend engineers to integrate ML into customer-facing systems.

• Write production-quality code within the existing codebase and contribute to architectural decisions.

• Over time, help recruit, mentor, and lead the ML team as the function expands.

• 8+ years of experience building ML systems in production environments.

• Experience standing up ML infrastructure at an early-stage startup or serving as the senior/lead ML engineer at a company.

• Strong software engineering fundamentals with production experience in languages such as Python, Java, or Type Script.

• Experience with cloud-based ML infrastructure (e.g., Sage Maker, Bedrock, Modal, Baseten, or similar platforms).

• Hands-on experience with ML and data frameworks such as PyTorch, Tensor Flow, Spark, or equivalent tools.

• Comfortable working across the stack — infrastructure, backend systems, and data platforms.

• Demonstrated ability to mentor engineers and elevate technical standards within a team.

• High autonomy and ownership mindset, with the ability to define direction and execute without predefined playbooks.

• Experience building ML systems for security, fraud detection, or adversarial environments.

• Experience working with LLMs in production (evaluation, fine-tuning, retrieval systems, guardrails).

• Background in real-time inference systems or high-throughput distributed systems.

• Experience making strategic build vs. buy infrastructure decisions.

• Previous startup experience in high-growth environments.

• Strategic AI backing:
  Supported by leading AI and venture investors shaping the future of AI infrastructure and cybersecurity.

• Founding-level impact:
  Build and define the ML function from zero in a company where ML is core to product value.

• Enterprise traction:
  Products already trusted by major banks, tech firms, and healthcare organizations.

• Massive market opportunity:
  Positioned in a rapidly expanding AI cybersecurity space.

• Leadership path:
  Opportunity to evolve into Head of ML as the organization scales.

• Ownership & autonomy:
  Direct influence over architecture, infrastructure, and long‑term technical direction.