Founding Machine Learning Engineer
- 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 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.
- 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.
- Competitive salary & equity options
- Health, Dental, and Vision
- 401k
- Hybrid flexibility
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