Machine Learning Research Scientist - Frontier LabSoftware Engineering Institute • United States

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Machine Learning Research Scientist - Frontier Lab

Software Engineering Institute

United States

United States

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divh2Machine Learning Research Scientist/h2pAt 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 test and evaluation, and technical advisory./ppAs a Machine Learning Research Scientist in the Frontier Lab, you will conduct applied AI/ML research and develop prototype capabilities that inform and improve real government workflows. You will execute work in mission contextdeveloping an appreciation for users, operational constraints, and intended outcomesand translate sponsor needs into technically credible approaches and evidence. This role spans the research-engineering spectrum: some MLRS hires may lean more research-heavy and others more engineering-heavy, but successful candidates collaborate effectively across both./ppFrontier Lab work spans several complementary focus areas, including:/pulliAgentic AI for mission workflows (e.g., planning, analysis, decision support) where autonomous and human-guided agents interact with tools, data systems, and operators./liliAI test, evaluation, verification, and validation (TEVV) to improve confidence in performance, robustness, uncertainty, and trustworthiness of ML-enabled systems./liliMission-tailored language models, including techniques to improve accuracy and reliability, reduce hallucinations, and integrate structured knowledge for operational tasks./liliMission modalities and multimodal learning, including sensor fusion and learning under noisy, sparse, or constrained data conditions (including synthetic data and weakly-/self-supervised approaches)./liliAI at the tactical edge, enabling capability under constrained compute/connectivity through efficient inference, compression, rapid adaptation, and update/redeploy patterns./li/ulpKey responsibilities / duties include:/pullistrongMission-context execution:/strong Execute tasks within the mission context, considering users, use cases, operational constraints, and intended outcomes. Translate sponsor goals into clear technical questions, measurable success criteria, and credible evaluation evidence./lilistrongApplied research and experimentation:/strong Design and conduct studies grounded in mission needs; form hypotheses, run controlled experiments, analyze results, and produce actionable recommendations./lilistrongPrototype capability development:/strong Build research prototypes, evaluation harnesses, and reference implementations that demonstrate feasibility and generate learning in realistic settings./lilistrongEvaluation and assurance (TEVV):/strong Develop and apply evaluation methodologies for ML systems (especially CV and LLMs), including metrics, benchmark design, robustness testing, uncertainty and calibration approaches, and repeatable test pipelines./lilistrongEngineering rigor appropriate to the task:/strong Write clear, maintainable code and documentation with a level of engineering discipline proportionate to the intended use. Emphasize reproducibility and handoff-ready artifacts suitable for downstream integration and operational hardening through formal DevSecOps processes./lilistrongIterative execution, self-direction, and time management:/strong Plan and deliver work in iterative cycles; manage priorities effectively; communicate status and risks early; and maintain momentum with minimal supervision./lilistrongCustomer translation and communication:/strong Communicate technical progress and results clearly to technical and non-technical stakeholders through briefings, demos, reports, and recommendations./lilistrongPublication and knowledge dissemination:/strong Identify opportunities to publish research insights and lessons learned at reputable venues (e.g., NeurIPS, ICLR, MLCON, etc.), subject to customer and releasability constraints./lilistrongTeam collaboration:/strong Contribute to technical discussions shaping tasking and delegation, support shared project goals, and provide guidance to junior teammates when appropriate./li/ulpRequirements include:/pulliEducation / Experience: BS in Electrical Engineering, Computer Science, Statistics, or related discipline with eight (8) years of experience in hands-on software development; OR MS in the same fields with five (5) years of experience; OR PhD with two (2) years of relevant experience./liliStrong foundation in machine learning and statistical learning, including experiment design and evaluation./liliDemonstrated ability to implement ML systems in Python using modern ML libraries (e.g., PyTorch/TensorFlow) and common scientific tooling./liliDemonstrated ability to communicate technical results clearly in written deliverables and presentations./liliAbility to work effectively with ambiguity and deliver results in iterative project cycles with strong self-direction./li/ulpKnowledge, skills, and abilities (KSAs) include:/pullistrongCommunication:/strong Explains technical content clearly; translates between mission problems and technical approaches./lilistrongScientific rigor:/strong Designs sound experiments; recognizes evaluation pitfalls (leakage, confounds, distribution shift)./lilistrongPractical execution:/strong Balances research quality with timelines and constraints; produces credible evidence and useful prototypes./lilistrongCollaboration:/strong Works well in interdisciplinary teams; contributes effectively to shared code and shared evaluation approaches./lilistrongAutonomy:/strong Executes independently with low oversight; manages time effectively; escalates risks early and seeks guidance when needed./li/ulpDesired experience includes:/pulliApplied ML research and prototyping for real operational workflows, including tool-integrated AI systems and human-in-the-loop settings./liliDesigning and operating evaluation pipelines for LLMs and/or CV models (benchmarking, regression testing, robustness checks, scenario-based evaluations)./liliLanguage model grounding and reliability techniques (structured knowledge integration, RAG, tool use, error analysis)./liliLearning under constrained/noisy data conditions (synthetic data, programmatic labeling, semi-/self-supervised learning)./liliEdge-relevant ML (compression, quantization, distillation, efficient inference, rapid adaptation patterns)./liliEvidence of research output: publications, technical reports, open-source contributions, or applied research artifacts./liliExperience working with government stakeholders or in high-assurance environments./li/ulpOther requirements include:/pulliFlexible to travel to SEI offices in Pittsburgh, PA and Washington, DC / Arlington, VA, sponsor sites, conferences, and offsite meetings (~10% travel)./liliYou must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week./liliYou will be subject to a background investigation and must be able to obtain and maintain a Department of War security clearance./li/ulpLocation: Arlington, VA, Pittsburgh, PA/ppJob Function: Software/Applications Development/Engineering/ppPosition Type: Staff Regular/ppFull time/Part time: Full time/ppPay Basis: Salary/p/div

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