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 andDoWworkflows. 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.

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 thatdemonstratefeasibility 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 rigorappropriate tothe 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 formalDevSecOpsprocesses.

• Iterative execution self-direction and time management:Plan and deliver work in iterative cycles; manage priorities effectively; communicate status and risks early; andmaintainmomentum 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:Identifyopportunities 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 whenappropriate.

Requirements

Education / Experience:

• BS in Electrical Engineering Computer Science Statistics or related discipline with eight (8) years of experience in hands-on software development; ORMS in the same fields with five (5) years of experience; ORPhD with two (2) years of relevant experience.

• Strong foundationin 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 forreal operationalworkflows 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/DoWstakeholders 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.