{"id":119677,"url":"https://alion.io/job/cmu-senior-machine-learning-research-scientist-frontier-lab","title":"Senior Machine Learning Research Scientist - Frontier Lab","company":{"id":46403,"name":"Software Engineering Institute","domain":"sei.cmu.edu","url":"https://alion.io/company/sei-cmu","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":87,"open_postings":45,"ghost_share":0,"stale_share":0.511,"repost_share":0,"time_to_fill_p50_days":32,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pittsburgh, United States","Arlington, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":134000,"max_usd":258000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Edge AI","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Sensor Fusion","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-05-20T00:00:00Z","employer_posted_date":"2026-05-20","last_verified_at":"2026-11-03T17:45:50Z","board_verified":true,"closed_at":null,"days_open":168,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":167},"description":"What We Do\nAt 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.\nTheFrontier Lab advances AI engineering and transitions frontier AI capabilities to government stakeholders through applied research, rapid prototyping, short-cycle TEVV, and technical advisory.\nPosition Summary\nAs 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.\nYou 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:\nAgentic AI for mission workflows (e.g., planning, analysis, decision support) where autonomous and human-guided agents interact with tools, data systems, and operators.\n\nAI test, evaluation, verification, and validation (TEVV) to improve confidence in performance, robustness, uncertainty, and trustworthiness of ML-enabled systems.\n\nMission-tailored language models, including techniques to improve accuracy and reliability, reduce hallucinations, and integrate structured knowledge for operational tasks.\n\nMission modalities and multimodal learning, including sensor fusion and learning under noisy, sparse, or constrained data conditions (including synthetic data and weakly-/self-supervised approaches).\n\nAI at the tactical edge, enabling capability under constrained compute/connectivity through efficient inference, compression, rapid adaptation, and update/redeploy patterns.\n\nKey Responsibilities / Duties\nSenior MLRS staff are expected tooperatewith a high degree of autonomy and technical ownership whileremaininghands-on in development, evaluation, and delivery.\nMission-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.\n\nTechnical leadership / Tech lead: Lead technical execution by defining technical tasking, sequencing work into realistic milestones,maintainingdelivery quality, and delegating appropriately across the team.\n\nApplied 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.\n\nEvaluation, assurance, and evidence:Establishcredible evaluation strategies and test pipelines that assess performance, robustness, reliability, and trustworthiness in mission-representative scenarios.\n\nCustomer-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.\n\nMentorship and talent development: Proactively mentor junior staff and teammates, raising the bar for research rigor, engineering practice, and delivery habits across project teams.\n\nState-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.\n\nSelf-direction and time management: Manage multiple priorities effectively, sustain steady execution cadence, and resolve blockers with minimal oversight.\n\nCommunity 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.\n\nRequirements\nEducation / Experience \n\nBSin Computer Science, Electrical Engineering, Statistics, or related field with10 yearsof relevant experience; OR MSwith8 yearsof relevant experience;OR PhDwith5 yearsof relevant experience.\n\nDeepexpertisein one or more Frontier Lab-aligned areas (agentic systems, LLM reliability/evaluation, CV evaluation, robustness/assurance, TEVV pipelines, multimodal learning, edge ML).\n\nStrong engineering capability- canbuild andmaintainhigh-quality prototypes, evaluation infrastructure, and repeatable experimentation workflows.\n\nStrong written and verbal communication skills; able torepresenttechnical work credibly to senior stakeholders.\n\nDemonstrated ability to lead technical workstreams and coordinate multi-person execution.\n\nKnowledge, Skills, & Abilities (KSAs)\nTechnical judgment: Makes sound architectural and methodological decisions; balances ambition with mission constraints.\n\nCustomer translation: Converts mission needs into tractable technical plans, measurable success criteria, and credible evaluation evidence.\n\nScientific leadership: Maintainsrigor;identifiesflawed assumptions; improves evaluation quality and research practices.\n\nMentorship & influence: Elevates team performance through hands-on guidance and strong technical standards.\n\nInitiative: Proactivelyidentifiesrisks/opportunities, proposes new work, and creates alignment without directive management.\n\nSelf-direction and time management: Plans work effectively under ambiguity,maintainsexecution cadence, and escalates risks early.\n\nDesired Experience\nLeading applied research projects resulting ineffectiveprototypes, mission-relevant evaluation outcomes, or transitioned methods.\n\nPublications 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).\n\nDesigning and operating TEVV efforts including evaluation pipelines, robustness analysis, calibration/uncertainty work, regression suites, and scenario-based evaluation protocols.\n\nBuilding agentic capabilities integrated with tools, data systems, and human workflows (decision support, planning, analytic contexts).\n\nExperience with secure or operational environments and delivery constraints typical of government settings.\n\nExperience shaping a technical roadmap or research portfolio aligned to sponsor priorities and lab strategy.\n\nOther Requirements\nFlexible to travel to SEI offices inPittsburgh, PAandWashington, DC / Arlington, VA, sponsor sites, conferences, and offsite meetings (~10% travel).\n\nYou must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week.\n\nYou will be subject to a background investigation and must be eligible to obtain andmaintaina Department of War) security clearance.\n\nLocation\nArlington, VA, Pittsburgh, PAJob Function\nSoftware/Applications Development/EngineeringPosition Type\nStaff - RegularFull time/Part time\nFull timePay Basis\nSalaryMore Information:\nPlease visit “Why Carnegie Mellon ” to learn more about becoming part of an institution inspiring innovations that change the world.\n\nClick here to view a listing of employee benefits\n\nCarnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.\n\nStatement of Assurance","description_format":"text","description_chars":7504,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Engineering Services","Research 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