Hiring: Senior Machine Learning Engineer, Defensive Agent Company: Horizon3.ai Location: United States Job Posted Time: 2026-09-15 13:21:54 Employment Type: Full-time / Remote Target Skills & Keywords : AWS, Azure, Data Pipeline, Datadog, Docker, ETL, FedRAMP, Fine-tuning, GCP, Grafana, GraphQL, Kubernetes, LLM, Machine Learning, Neo4j, PostgreSQL, Prometheus, Python, SQL, vLLM About the job Experience: •5+ yrs professional software engineering experience, with strong production Python. •Demonstrated experience taking ML or LLM-backed systems from prototype to production and operating them. •Applied hands-on capability in ML pipelines and tooling: training or fine-tuning workflows, experiment tracking, artifact and model registries, and reproducible data preparation. •Solid proficiency in SQL and experience with production data pipelines. •Hands-on post-training experience: supervised fine-tuning, distillation, preference optimization, or RL, including the infrastructure around the runs. Required Skills: •You'll work directly with our AI researchers and closely with backend and infrastructure engineers. You are not being hired to do research, and you are not being hired to do generic platform work. You own the path from model to production. •Build and own the training and post-training pipelines — data preparation, fine-tuning and preference optimization runs, experiment tracking, artifact management, and reproducibility. •Build the inference and serving layer: model gateway with provider routing and fallback, regional pinning for data residency, batching, and caching. •Own the release path for model-layer artifacts: version prompts, model selections, and tool definitions as deployable config; run shadow and canary deployments by tenant; make rollback fast and boring. •Build monitoring for the model layer — behavioral drift, regression detection, output quality signals, latency, and per-tenant token and cost accounting with budget enforcement. •Build the data and context pipelines that feed inference, including retrieval and embedding infrastructure over attack path, configuration, and remediation data, with tenant isolation enforced end to end. •Optimize cost and latency across the inference path, and make the tradeoffs visible so product decisions are made with real numbers. •Develop core product features in ETL and GraphQL where model outputs, run history, and evaluation results need to reach the product and internal tooling. Qualifications: •Bachelor's Degree in Computer Science, Computer Engineering or related field, or equivalent practical experience. Compensation: •$211,000 - $249,000 / year •Flexible work environment (work from home / hybrid options) •Competitive Compensation: We offer competitive salary, equity and benefits Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!