Senior ML 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** Arlington, VA, Pittsburgh, PA **Job Function** Software/Applications Development/Engineering **Position Type** Staff – Regular **Full time/Part time** Full time **Pay Basis** Salary **More Information:**  ---------------------- * Please visit **“[Why Carnegie Mellon](http://www.cmu.edu/jobs/why-cmu/index.html)”** to learn more about becoming part of an institution inspiring innovations that change the world.  * Click [here](https://www.cmu.edu/jobs/benefits-at-a-glance/) to view a listing of employee benefits * **Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran**.  * [**Statement of Assurance**](https://www.cmu.edu/policies/administrative-and-governance/statement-of-assurance.html)