Senior 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 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, 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

Job Function

Position Type

Full time/Part time

Pay Basis

More Information: 

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• Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran. 

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