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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.

TheFrontier Labadvances 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 andDoWmissions.This role spans the research-engineering spectrum: someSRMLRS hires may lean more research-heavy and others more engineering-heavy, but successful candidates collaborate effectively across both.

You willoperatewith high autonomy, represent technical work with customers and stakeholders, and help guide Frontier Lab research direction-whileremaininghands-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 tooperatewith a high degree of autonomy and technical ownership whileremaininghands-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-artawareness 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 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:Maintainsrigor;identifiesflawed assumptions; improves evaluation quality and research practices.

• Mentorship & influence:Elevates team performance through hands-on guidance and strong technical standards.

• Initiative:Proactivelyidentifiesrisks/opportunities, proposes new work, and creates alignment without directive management.

• Self-direction and time management: Plans work effectively under ambiguity,maintainsexecution cadence, and escalates risks early.
  

Desired Experience

• Leading applied research projects resulting ineffectiveprototypes, 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 inPittsburgh, PAandWashington, 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 andmaintaina Department of War)security clearance.