Founding Machine Learning Engineer

Pragmatike•Washington, DC

2d•Remote

About The Position

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.

Requirements

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 TypeScript.
Experience with cloud-based ML infrastructure (e.g., SageMaker, Bedrock, Modal, Baseten, or similar platforms).
Hands-on experience with ML and data frameworks such as PyTorch, TensorFlow, 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.

Nice To Haves

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.

Responsibilities

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.