**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 TEVV, and technical advisory. **Position Summary** As a Senior Machine Learning Research Scientist in the Frontier Lab, you will serve as a senior individual contributor and technical leader, shaping and executing applied research and prototype capability development for government and DoW missions. This role spans the research-engineering spectrum: some SR MLRS hires may lean more research-heavy and others more engineering-heavy, but successful candidates collaborate effectively across both. You will operate with high autonomy, represent technical work with customers and stakeholders, and help guide Frontier Lab research direction-while remaining hands-on in development, evaluation, and delivery. Your work may span Frontier Lab focus areas such as: + 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** Senior MLRS staff are expected to operate with a high degree of autonomy and technical ownership while remaining hands-on in development, evaluation, and delivery. + **Mission-context execution** : Execute work within the operational context-understanding users, workflows, constraints, success criteria, and outcomes-so technical decisions are grounded in real mission needs. + **Technical leadership / Tech lead** : Lead technical execution by defining technical tasking, sequencing work into realistic milestones,maintainingdelivery quality, and delegating appropriately across the team. + **Applied research and prototyping** : Design and run studies, build convincingprototypesand reference implementations, and produce evidence-backed insights that can be matured and transitioned into operational settings. + **Evaluation, assurance, and evidence** :Establishcredible evaluation strategies and test pipelines that assess performance, robustness, reliability, and trustworthiness in mission-representative scenarios. + **Customer-facing technical ownership** : Serve as the primary technical interface whenappropriate; translate mission goals into measurable technical outcomes; communicate progress, decisions, and risks clearly to stakeholders. + **Mentorship and talent development** : Proactively mentor junior staff and teammates, raising the bar for research rigor, engineering practice, and delivery habits across project teams. + **State-of-the-art** **awareness and agenda shaping** :Maintainstrong awareness of frontier developments aligned to the Frontier Lab, share insights with the lab, and help shape research directions and future work selection. + **Self-direction and time management** : Manage multiple priorities effectively, sustain steady execution cadence, and resolve blockers with minimal oversight. + **Community building (internal and external)** : Build a strong research culture through internal talks, reading groups, and workshops; and engage with external AI/ML communities (professional societies, consortiums, working groups, and conferences) to strengthen collaboration pathways and keep the lab connected to emerging practice. **Requirements** + Education / Experience + BSin Computer Science, Electrical Engineering, Statistics, or related field with10 yearsof relevant experience; OR MSwith8 yearsof relevant experience; OR PhDwith5 yearsof relevant experience. + Deepexpertisein one or more Frontier Lab-aligned areas (agentic systems, LLM reliability/evaluation, CV evaluation, robustness/assurance, TEVV pipelines, multimodal learning, edge ML). + Strong engineering capability- canbuild andmaintainhigh-quality prototypes, evaluation infrastructure, and repeatable experimentation workflows. + Strong written and verbal communication skills; able torepresenttechnical work credibly to senior stakeholders. + Demonstrated ability to