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, maintaining delivery quality, and delegating appropriately across the team.
Applied research and prototyping: Design and run studies, build convincing prototypes and reference implementations, and produce evidence-backed insights that can be matured and transitioned into operational settings.
Evaluation, assurance, and evidence: Establish credible 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 when appropriate; 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: Maintain strong 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
BS in Computer Science, Electrical Engineering, Statistics, or related field with 10 years of relevant experience; OR MS with 8 years of relevant experience; OR PhD with 5 years of relevant experience.
Deep expertise in 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 – can build and maintain high-quality prototypes, evaluation infrastructure, and repeatable experimentation workflows.
Strong written and verbal communication skills; able to represent technical work credibly to senior stakeholders.
Demonstrated ability to lead technical workstreams and coordinate multi-person execution.
Knowledge, Skills, & Abilities (KSAs)
Technical judgment: Makes sound architectural and methodological decisions; balances ambition with mission constraints.
Customer translation: Converts mission needs into tractable technical plans, measurable success criteria, and credible evaluation evidence.
Scientific leadership: Maintains rigor; identifies flawed assumptions; improves evaluation quality and research practices.
Mentorship & influence: Elevates team performance through hands-on guidance and strong technical standards.
Initiative: Proactively identifies risks/opportunities, proposes new work, and creates alignment without directive management.
Self-direction and time management: Plans work effectively under ambiguity, maintains execution cadence, and escalates risks early.
Desired Experience
Leading applied research projects resulting in effective prototypes, mission-relevant evaluation outcomes, or transitioned methods.
Publications at strong venues (e.g., NeurIPS / ICLR / ICML, relevant workshops, MLCON), and/or demonstrable impact through applied research artifacts (benchmarks, evaluation suites, open-source, technical reports).
Designing and operating TEVV efforts including evaluation pipelines, robustness analysis, calibration/uncertainty work, regression suites, and scenario-based evaluation protocols.
Building agentic capabilities integrated with tools, data systems, and human workflows (decision support, planning, analytic contexts).
Experience with secure or operational environments and delivery constraints typical of government settings.
Experience shaping a technical roadmap or research portfolio aligned to sponsor priorities and lab strategy.
Other Requirements
Flexible to travel to SEI offices in Pittsburgh, PA and Washington, DC / Arlington, VA, sponsor sites, conferences, and offsite meetings (~10% travel).
You must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week.
You will be subject to a background investigation and must be eligible to obtain and maintain a Department of War) security clearance.
Location
- Arlington, VA, Pittsburgh, PA
- Job Function
- Software/Applications Development/Engineering
- Position Type
- Staff – Regular
- Full time/Part time
- Full time
- Pay Basis
- Salary
- More Information:
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