Applied AI Engineer

About The Company Hackbook is focused on protecting critical infrastructure, industrial organizations, and government entities from cyber threats, helping customers drive secure digital transformation. The company provides advanced network and asset visibility, threat detection, and actionable insights tailored to OT and IoT environments. Clients rely on HackBook to reduce risk and complexity while strengthening operational resilience. The Role You’ll build and ship language-model-powered systems that strengthen HackBook’s mission-critical cyber capabilities for national security. You’ll own the end-to-end workflow—from curating specialized datasets and post-training models to deploying reliable inference and retrieval systems in production. You’ll partner closely with engineering to translate real operational needs into high-performing AI features, operating across cloud and on-premises environments where speed, correctness, and security matter. Who You Are You’re motivated by real-world outcomes and want your work to directly impact national security missions. You care about rigor: clean data, measurable evaluation, and repeatable experiments beat demos. You balance research curiosity with product instincts—you ship, observe, iterate, and harden. You’re comfortable working across cloud and on-premises constraints and adapting to the environment. You communicate clearly with engineers and non-ML partners, and you write documentation people use. You think in systems: models, retrieval, infrastructure, and feedback loops all have to work together. You thrive in fast-moving teams with high standards, direct feedback, and high ownership. What You’ll Do Create, clean, and maintain high-quality training and evaluation datasets for specialized AI use cases. Fine-tune language models (small specialized through medium foundation models) for mission needs. Implement post-training and alignment approaches to improve task performance and reliability. Build retrieval-augmented generation (RAG) systems that ground model outputs in external knowledge. Develop and optimize model serving infrastructure for production deployments. Design evaluation frameworks and test harnesses to measure quality, latency, and regressions. Integrate AI capabilities into applications and workflows using modern orchestration frameworks. Collaborate with cross-functional partners to identify high-leverage use cases and deliver solutions. Produce clear technical documentation for models, datasets, and operational processes. Must Have You have must have experience building and supporting ML/AI-enabled applications. You have strong Python skills and deep learning experience with PyTorch, TensorFlow, or JAX. You have hands-on experience with LLM post-training methods (e.g., continued pre-training, SFT, RLHF, DPO, PPO, GRPO). You have experience curating, cleaning, and preprocessing datasets for training and evaluation. You have working knowledge of relational, graph, and vector database concepts. You have experience designing or using evaluation metrics and testing procedures for LLMs and agents. You have experience integrating LLM/agent systems using frameworks like Pydantic-AI, LangChain/LangGraph, or CrewAI. You have a Bachelor’s degree in Computer Science, Software Engineering, or a related field (or equivalent practical experience). Nice To Have You have deployed models to production and supported them through real-world usage and incidents. You have experience with distributed training systems and performance debugging at scale. You have implemented quantization or other optimization techniques to improve inference efficiency. You have strong prompt engineering and model alignment instincts for reliability and control. You have experience building MLOps/LLMOps/AgentOps practices (versioning, rollout, monitoring). Tech Environment (You Might Work With) Deep learning stacks: PyTorch, TensorFlow, JAX LLMOps and serving: vLLM, TensorRT, ONNX Retrieval and storage: pgvector, ChromaDB, Pinecone, Milvus, Weaviate; relational/graph databases Orchestration: Pydantic-AI, LangChain/LangGraph, CrewAI Infra: Docker, Kubernetes; cloud platforms (AWS, GCP, Azure) Experiment and artifact tracking: dataset/prompt/model versioning Security / Work Environment Must be eligible to obtain and maintain a U.S. Government security clearance. Benefits  Employees (and their eligible dependents) can enroll in medical, dental, and vision insurance  2 weeks paid time off built into the end of each year   10 paid holidays throughout the calendar year  Employees can enroll in HackBook’s 401k plan Life at HackBook We want every employee to achieve their best outcomes, that’s why we celebrate individuals’ strengths, skills, and interests, from your first interview to your longterm growth, rather than rely on traditional career ladders. In keeping consistent with HackBook’s values and culture, we believe employees are better together and in-person work affords the opportunity for more creative outcomes. Therefore, we encourage employees to work from our offices in Miami,FL or New York, NY to foster connectivity and innovation. We do offer hybrid options (WFH a day or two a week), allowing our employees to strike the right trade-off for their personal productivity.  If you want to empower the world's most important institutions, you belong here. HackBook values excellence regardless of background. We are proud to be an Equal Opportunity Employer for all, including but not limited to Veterans and those with disabilities. HackBook is committed to making the application and hiring process accessible to everyone and will provide a reasonable accommodation for those living with a disability. If you need an accommodation for the application or hiring process , please reach out and let us know how we can help. Please note that you will NEVER be asked to submit a payment or share financial information to participate in our interview process. If you suspect that you've been contacted by a scammer, we recommend you cease all communication with the individual and consider reporting them to the relevant authorities. 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