Frontier AI Research Scientist for Mission Prototyping

Software Engineering Institute | Carnegie Mellon University; Arlington (VA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Onsite work in Arlington

Travel opportunities to SEI offices

Job summary

Software Engineering Institute, part of Carnegie Mellon University, seeks a Senior Machine Learning Research Scientist for the Frontier Lab in Arlington/Pittsburgh. You will advance applied AI research, prototype capabilities, and TEVV-driven evaluation for government missions, collaborating across research and engineering teams.

You will operate with high autonomy, mentor staff, and communicate progress to stakeholders, while remaining hands-on in development, evaluation, and delivery.

Qualifications

• 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 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.

Responsibilities

• 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.

Skills

Technical judgment

Customer translation

Scientific leadership

Mentorship & influence

Initiative

Self-direction

Education

BS in CS/EE/Statistics or related field

MS in related field

PhD in related field

Job description

Software Engineering Institute, part of Carnegie Mellon University, seeks a Senior Machine Learning Research Scientist for the Frontier Lab in Arlington/Pittsburgh. You will advance applied AI research, prototype capabilities, and TEVV-driven evaluation for government missions, collaborating across research and engineering teams.

You will operate with high autonomy, mentor staff, and communicate progress to stakeholders, while remaining hands-on in development, evaluation, and delivery.