Machine Learning 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 test and evaluation, and technical advisory.

Position Summary

As 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 and Do W workflows. You will execute work in mission context-developing an appreciation for users, operational constraints, and intended outcomes-and 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.

Frontier Lab work spans several complementary focus areas, including:

• 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

• Mission-context execution: 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.

• Applied research and experimentation: Design and conduct studies grounded in mission needs; form hypotheses, run controlled experiments, analyze results, and produce actionable recommendations.

• Prototype capability development: Build research prototypes, evaluation harnesses, and reference implementations that demonstrate feasibility and generate learning in realistic settings.

• Evaluation and assurance (TEVV): 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.

• Engineering rigor appropriate to the task: 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.

• Iterative execution, self-direction, and time management: Plan and deliver work in iterative cycles; manage priorities effectively; communicate status and risks early; and maintain momentum with minimal supervision.

• Customer translation and communication: Communicate technical progress and results clearly to technical and non-technical stakeholders through briefings, demos, reports, and recommendations.

• Publication and knowledge dissemination: Identify opportunities to publish research insights and lessons learned at reputable venues (e.g., NeurIPS , ICLR, MLCON, etc.), subject to customer and releasability constraints.

• Team collaboration: Contribute to technical discussions shaping tasking and delegation, support shared project goals, and provide guidance to junior teammates when appropriate .

Requirements

Education / 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.

• Strong foundation in machine learning and statistical learning, including experiment design and evaluation.

• Demonstrated ability to implement ML systems in Python using modern ML libraries (e.g., PyTorch / TensorFlow) and common scientific tooling.

• Demonstrated ability to communicate technical results clearly in written deliverables and presentations.

• Ability to work effectively with ambiguity and deliver results in iterative project cycles with strong self-direction.

Knowledge, Skills, & Abilities (KSAs)

• Communication: Explains technical content clearly; translates between mission problems and technical approaches.

• Scientific rigor: Designs sound experiments; recognizes evaluation pitfalls (leakage, confounds, distribution shift).

• Practical execution: Balances research quality with timelines and constraints; produces credible evidence and useful prototypes.

• Collaboration: Works well in interdisciplinary teams; contributes effectively to shared code and shared evaluation approaches.

• Autonomy: Executes independently with low oversight; manages time effectively; escalates risks early and seeks guidance when needed.

Desired Experience

• Applied ML research and prototyping for real operational workflows, including tool-integrated AI systems and human-in-the-loop settings.

• Designing and operating evaluation pipelines for LLMs and/or CV models (benchmarking, regression testing, robustness checks, scenario-based evaluations).

• Language model grounding and reliability techniques (structured knowledge integration, RAG, tool use, error analysis).

• Learning under constrained/noisy data conditions (synthetic data, programmatic labeling, semi-/self-supervised learning).

• Edge-relevant ML (compression, quantization, distillation, efficient inference, rapid adaptation patterns).

• Evidence of research output: publications, technical reports, open-source contributions, or applied research artifacts.

• Experience working with government/ Do W stakeholders or in high-assurance environments.

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 able 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
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