Machine Learning Research Scientist - Frontier Lab

What We Do 

At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering challenges related to building, deploying, and sustaining AI-enabled systems for high-impact government missions. 

The Frontier Lab advances AI engineering and transitions frontier AI capabilities to government stakeholders through applied research, rapid prototyping, short-cycle test and evaluation, and technical advisory. 

Position Summary 

As a Machine Learning Research Scientist in the Frontier Lab, you will conduct applied AI/ML research and develop prototype capabilities that inform and improve real government and DoW workflows. You will execute work in mission context—developing an appreciation for users, operational constraints, and intended outcomes—and translate sponsor needs into technically credible approaches and evidence. This role spans the research-engineering spectrum: some MLRS hires may lean more research-heavy and others more engineering-heavy, but successful candidates collaborate effectively across both. 

Frontier Lab work spans several complementary focus areas, including: 

• Agentic AI for mission workflows (e.g., planning, analysis, decision support) where autonomous and human-guided agents interact with tools, data systems, and operators. 

• AI test, evaluation, verification, and validation (TEVV) to improve confidence in performance, robustness, uncertainty, and trustworthiness of ML-enabled systems. 

• Mission-tailored language models, including techniques to improve accuracy and reliability, reduce hallucinations, and integrate structured knowledge for operational tasks. 

• Mission modalities and multimodal learning, including sensor fusion and learning under noisy, sparse, or constrained data conditions (including synthetic data and weakly-/self-supervised approaches). 

• AI at the tactical edge, enabling capability under constrained compute/connectivity through efficient inference, compression, rapid adaptation, and update/redeploy patterns. 

Key Responsibilities / Duties 

• Mission-context execution: Execute tasks within the mission context, considering users, use cases, operational constraints, and intended outcomes. Translate sponsor goals into clear technical questions, measurable success criteria, and credible evaluation evidence. 

• Applied research and experimentation: Design and conduct studies grounded in mission needs; form hypotheses, run controlled experiments, analyze results, and produce actionable recommendations. 

• Prototype capability development: Build research prototypes, evaluation harnesses, and reference implementations that demonstrate feasibility and generate learning in realistic settings. 

• Evaluation and assurance (TEVV): Develop and apply evaluation methodologies for ML systems (especially CV and LLMs), including metrics, benchmark design, robustness testing, uncertainty and calibration approaches, and repeatable test pipelines. 

• Engineering rigor appropriate to the task: Write clear, maintainable code and documentation with a level of engineering discipline proportionate to the intended use. Emphasize reproducibility and handoff-ready artifacts suitable for downstream integration and operational hardening through formal DevSecOps processes. 

• Iterative execution, self-direction, and time management: Plan and deliver work in iterative cycles; manage priorities effectively; communicate status and risks early; and maintain momentum with minimal supervision.