AI Software Engineer

Gallatin•Washington, DC

29d•$80,000 - $210,000•Onsite

About The Position

We’re looking for an AI/ML Software Engineer to play a foundational role in developing and deploying AI-powered solutions for our core product. You’ll work across the full AI/ML lifecycle—from defining and building models to evaluating and deploying large scale ML pipelines and real-time inference systems—while collaborating with cross-functional teams to deliver impactful, real-world AI applications.

Requirements

Strong Programming Skills
Expertise in at least one: Python, C++, or C.
ML Framework Proficiency
Hands-on experience with AI/ML training and fine-tuning frameworks, including:
PyTorch, TensorFlow, CUDA, Jupyter Notebooks
Large Language Models (LLMs) and Open-Source Models (Llama, Anthropic, Mistral)
LangChain, Retrieval-Augmented Generation (RAG), Hugging Face, Exo Labs
MLOps & Data Engineering Knowledge
Experience with data processing and pipeline frameworks such as Apache Kafka, Apache Airflow, AWS Kinesis, pandas, and dbt.
Understanding of AI model performance monitoring, data drift detection, and observability tools like Grafana or Kibana.
AI Fundamentals & Security Awareness
Deep understanding of deep neural networks (DNNs), LLMs, over/underfitting, prompt engineering, and LLM security (jailbreaking risks and protections).
Growth Mindset
Passion for continuous learning and staying current with the latest advancements in AI/ML, as well as teaching others

Nice To Haves

Experience mentoring and upskilling junior engineers.
Familiarity with legacy systems and working with public sector customers.
Contributions to open-source (AI/ML) projects.

Responsibilities

Build and Deploy AI/ML Models
Own and develop an AI model aligned with our core product needs within the first three months.
Validate AI-driven product features through real-world testing and feedback from warfighters.
Establish scalable MLOps pipelines and real-time inference services to streamline model training, deployment, runtime, and monitoring.
Own high model reliability and uptime by implementing monitoring across your work.
Data Collection & Processing
Identify and integrate structured, unstructured, real-time, and batch data sources.
Work with internal logs, APIs, user interactions, and third-party datasets to improve model training quality.
Continuous Improvement & Best Practices
Stay ahead of AI trends and emerging technologies to improve model performance.
Document and share best practices for AI/ML development.
Contribute to the hiring and mentoring of AI/ML talent as we grow our team.
Teach the wider team about the latest trends and significance in novel approaches in AI.

Benefits

Competitive compensation incl. generous options grant, 100% employer-paid health insurance premiums, 401k, opportunities for rapid career growth, unlimited PTO, free lunches and snacks in the office.

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