Senior Data Scientist

Second Front Systems

5d•$137,000 - $180,000•Remote

About The Position

Second Front Systems (2F) is seeking an ambitious and visionary Data Scientist to join our mission-driven team. We are a dynamic, fast-growing entrepreneurial company at the intersection of cutting-edge technology and national security, committed to delivering transformative solutions that empower our nation’s defenders. This is an opportunity to play a pivotal role in shaping the future of a company that is redefining the way software is delivered and secured in the defense sector. At 2F, we thrive on innovation and purpose, combining a startup’s agility with a clear mission to support national security. You will be at the forefront of driving the AI strategy behind the deployment and scaling of our and scaling of our revolutionary Game Warden platform—an industry-leading tool that is accelerating the secure adoption of mission-critical SaaS solutions for the U.S. government. If you’re ready to contribute to a team that values ingenuity, collaboration, and impact, we want to hear from you. Second Front Systems is building toward an AI-native future, one where artificial intelligence and machine learning are not features bolted onto our products, but the foundation upon which every customer experience, internal workflow, and platform capability is built. Research and Development is the engine driving that transformation. This is a hands-on research and engineering role with organizational reach. The right person is equally comfortable designing an evaluation framework, advising a Product team on model selection, and shipping production-grade AI systems. Note: This role requires U.S. citizenship due to government contract requirements. Additionally, candidates must reside in one of our approved hiring hubs: DC/Maryland/Virginia Raleigh/Durham/Chapel Hill, NC Denver/Colorado Springs, CO Dallas/Fort Worth, TX

Requirements

5 years, or greater, experience in applied machine learning, data science, or AI engineering, with at least 2 years working with large language models in production.
Hands-on experience building agentic AI systems with tool-calling agents, multi-step reasoning pipelines, or DAG-based orchestration (LangGraph, LangChain, or equivalent).
Strong proficiency in Python and the modern ML/AI ecosystem.
Experience with Retrieval-Augmented Generation, including vector databases, embedding models, chunking strategies, and retrieval evaluation.
Experience with foundation model APIs across multiple providers: AWS Bedrock, OpenAI, Anthropic, Cohere, and Meta.
Proven ability to design and implement quantitative evaluation frameworks for LLM or agentic system outputs.
Demonstrated ability to communicate AI concepts and recommendations to non-technical stakeholders and cross-functional teams.
Strong command of statistical reasoning, probabilistic modeling, and experiment design.

Nice To Haves

Experience driving AI adoption or integration across a product organization, not just building models, but enabling others to use them effectively.
Experience with cybersecurity data, vulnerability management, or threat intelligence.
Experience with deployment patterns for containerized applications.
Experience designing or operating multi-tenant and single-tenant LLM serving infrastructure.
Experience with multi-model ensemble methods or multi-agent coordination.
Experience working in or with the Department of Defense, federal agencies, or cleared environments.
Active security clearance or eligibility to obtain one.
Experience with model fine-tuning, RLHF, or PEFT methods.
Familiarity with compliance frameworks such as NIST 800-53, NATO D32, or FedRAMP.
Have a strong interest in matters of national security

Responsibilities

Design, develop, and improve agentic AI systems using LangGraph and LangChain, with a focus on reliability, traceability, and measurable output quality.
Advance our existing capabilities including the DAG driven AI and chatbots by improving reasoning, tool-calling accuracy, and inference confidence.
Develop and mature evaluation frameworks to assess AI-generated outputs across dimensions including groundedness, faithfulness, relevance, context precision, and context recall.
Improve and extend our multi-LLM ensemble system including consensus scoring methods, model weighting, and aggregation strategies.
Design and implement fine-tuning and prompt optimization pipelines for domain-specific cybersecurity and compliance use cases.
Develop AI/ML components for RAG systems, including embedding strategies, retrieval optimization, chunking, and re-ranking.
Partner with Product and Engineering teams to identify high-leverage opportunities to introduce AI across onboarding, deployment, monitoring, configuration, and remediation workflows.
Develop and document reusable AI-native components, integration patterns, and deployment blueprints, including single-tenant and multi-tenant LLM serving architectures on AWS Bedrock, Cohere, Anthropic, and OpenAI-compatible providers to accelerate adoption across teams.
Define and promote standards for responsible AI integration: evaluation methodology, explainability, audit logging, data privacy, and model governance.
Act as an internal AI advisor helping Product teams navigate model selection, agent design, RAG implementation, and output quality.
Contribute to building the organizational muscle for AI adoption: documentation, knowledge transfer, and structured learning.