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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Research Scientist - Frontier Lab - **Company:** Carnegie Mellon University - **Location:** Pittsburgh, PA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Systems, Decision Support Systems, Machine Learning, Language Modeling, Open Source Technology, Sensor Fusion, Multimodal Learning, Evaluation Pipelines, Large Language Models, Agentic-AI, Information Technology - **Published:** October 16, 2026 - **Apply:** https://dejobs.org/x/x/99E6B3A249504D6F995D3490380CFBC6/job/ ## About the Role * 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. ## Description 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., 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,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-art awareness 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.