Senior Machine Learning Engineer, Defensive Agent

🕒 September 14

🇺🇸 United States – Remote

💵 $211k - $249k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 22%

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Horizon3.ai

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2019

🔒 Cybersecurity

🤖 Artificial Intelligence

☁️ SaaS

Cybersecurity • Artificial Intelligence • SaaS

Horizon3. ai is a cybersecurity company that specializes in autonomous penetration testing through its platform, NodeZero. The company's mission is to proactively identify and address attack vectors before they can be exploited, allowing organizations to continuously assess their security posture across various environments including cloud, IoT, and on-premises systems. Founded by veterans from the US Special Operations and National Security sectors, Horizon3. ai offers a self-service SaaS solution that provides organizations with insights into their security vulnerabilities without requiring persistent or credentialed agents.

📋 Description

• Build and own training and post-training pipelines, including data preparation, fine-tuning, preference optimization, experiment tracking, artifact management, and reproducibility • Build the inference and serving layer with provider routing, fallback, regional pinning for data residency, batching, and caching • Own the model-artifact release path, including versioned prompts, model selections, tool definitions, shadow deployments, canary deployments, and rollback • Build model-layer monitoring for behavioral drift, regression detection, output quality, latency, and per-tenant token and cost accounting with budget enforcement • Build data and context pipelines for inference, including retrieval and embedding infrastructure over attack path, configuration, and remediation data • Enforce tenant isolation end to end • Optimize cost and latency across the inference path and make tradeoffs visible • Develop product features in ETL and GraphQL for model outputs, run history, and evaluation results • Partner with AI researchers to move prototypes into production and feed production constraints and failure data back into research direction • Get defensive models into production and keep them operating across thousands of customer tenants

🎯 Requirements

• Bachelor's Degree in Computer Science, Computer Engineering or related field, or equivalent practical experience • 5+ years of professional software engineering experience • Strong production Python • Demonstrated experience taking ML or LLM-backed systems from prototype to production and operating them • Hands-on experience with ML pipelines and tooling, including training or fine-tuning workflows, experiment tracking, artifact and model registries, and reproducible data preparation • Experience building and operating production inference or model-serving infrastructure, including latency and cost optimization • Experience building applications on AWS, Azure, or GCP • Experience with Docker and Kubernetes • Solid proficiency in SQL and experience with production data pipelines • Experience operationalizing LLM or agentic systems • Hands-on post-training experience, including supervised fine-tuning, distillation, preference optimization, or reinforcement learning • Experience with model gateways or multi-provider routing, and self-hosted or customer-hosted inference such as vLLM, TGI, or Bedrock • Experience with GPU infrastructure, quantization, or inference optimization • Experience with relational databases such as PostgreSQL and graph databases such as Neo4j • Experience with GraphQL backends • Experience with observability tooling such as Datadog, Prometheus, or Grafana and distributed tracing • Experience shipping ML into regulated, air-gapped, or customer-controlled environments, or under compliance regimes such as FedRAMP • Legally authorized to work in the United States • Bachelor's degree or equivalent practical experience

🏖️ Benefits

• Equity package in the form of stock options for all full-time roles • Health, vision and dental insurance for you and your family • Flexible vacation policy • Generous parental leave • Remote and hybrid work models depending on role and location • Career development opportunities • Inclusive and collaborative work culture

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