AI Engineer

Location: On-site in Arlington, Virginia, United States; limited to U.S. citizens and requires eligibility to obtain and maintain a U.S. Government security clearance.

Salary: $159,000–$195,000 base salary annually; estimated total compensation of $204,000–$263,000 including target bonus and equity.

Company

hirify.global industrializes offensive cyber operations for the United States and its allies, building AI-powered capabilities for national security missions.

What you will do

• Create, clean, and maintain training and evaluation datasets for specialized AI use cases.
• Fine-tune language models and implement post-training and alignment methods such as SFT, RLHF, DPO, PPO, and GRPO.
• Build retrieval-augmented generation systems and integrate LLM and agent capabilities into applications and workflows.
• Develop and optimize model-serving infrastructure for production deployments across cloud and on-premises environments.
• Design evaluation frameworks and test harnesses to measure quality, latency, and regressions.
• Collaborate with product and engineering partners and document models, datasets, and operational processes.

Requirements

• 4+ years of professional software development experience building and supporting ML/AI-enabled applications.
• Strong Python skills and deep learning experience with PyTorch, TensorFlow, or JAX.
• Hands-on experience with LLM post-training, dataset preparation, evaluation metrics, and testing procedures for LLMs and agents.
• Working knowledge of relational, graph, and vector databases, plus experience integrating agent systems with modern orchestration frameworks.
• Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
• U.S. citizenship and eligibility to obtain and maintain a U.S. Government security clearance are required.

Nice to have

• Experience deploying and supporting models in production, including real-world incidents.
• Distributed training and performance debugging at scale.
• Quantization and other inference-efficiency optimization techniques.
• MLOps, LLMOps, or AgentOps practices including versioning, rollout, and monitoring.
• Experience with vLLM, TensorRT, ONNX, pgvector, ChromaDB, Pinecone, Milvus, Weaviate, Docker, Kubernetes, AWS, GCP, or Azure.

Culture & Benefits

• Mission-focused work supporting U.S. national security and real-world cyber operations.
• Fast-moving environment with high standards, direct feedback, and high ownership.
• Medical, dental, vision, life, AD&D, and disability coverage options.
• Paid parental leave, paid holidays, and flexible PTO.
• 401(k) options with pre-tax and Roth contributions, plus HSA, FSA, and dependent-care FSA options.