"Assistant Machine Learning Engineer - 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 an Assistant Machine Learning Engineer in the Frontier Lab, you will be a technical contributor supporting applied AI research, prototype development, and AI evaluation work for real government and DoW workflows. You will execute work in mission context--learning the users, operational constraints, and intended outcomes--so that your technical contributions are grounded in how systems are actually used. This role is well-suited for early-career engineers who enjoy building and evaluating AI/ML systems, want exposure to frontier methods, and are developing one or more areas of technical depth.

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

Assistant MLEs are expected to be reliable technical contributors who can execute scoped work with low supervision and grow technical depth over time.

• Mission-context execution: Execute tasks with awareness of the operational context--users, workflows, constraints, success criteria, and outcomes--so technical decisions are relevant and defensible.
• Technical contribution to project execution: Implement, test, and iterate on ML capabilities, prototype systems, and evaluation tooling aligned to project goals and milestones.
• Support applied prototyping and experimentation: Contribute to research prototypes and experimentation workflows by implementing components, running experiments, and assisting with analysis and reporting.
• Participate in planning and shaping tasking: Contribute to technical discussions that shape work breakdowns and sprint plans; raise risks, dependencies, and test considerations early.
• Iterative execution and self-management: Execute scoped work in sprint cycles with periodic check-ins while proactively communicating status, blockers, and tradeoffs clearly.
• Documentation and communication: Present technical progress through demos, briefings, and concise written artifacts that enable others to build on your work.
• Learning and growth: Identify one or more areas of expertise to deepen over time; actively seek mentorship and learning opportunities aligned to lab priorities.
• Community participation: Participate in lab extracurricular activities (e.g., reading groups, internal talks, technical sharing) and contribute to a strong technical culture.

Requirements

Education / Experience:

• BS in Electrical Engineering, Computer Science, Statistics, or related discipline

Technical Requirements

• Demonstrated ability to write software in Python, including working in a collaborative codebase.
• Familiarity with modern ML tools and workflows (e.g., PyTorch/TensorFlow, common data tooling, experiment tracking concepts).
• Ability to implement defined approaches, execute experiments, and contribute to evaluation and reporting.
• Ability to communicate technical results clearly and work effectively as part of a project team.
• Current or recent experience working on national security related machine learning projects within a federally funded research and development (FFRDC) environment.

Knowledge, Skills, & Abilities (KSAs)

• Execution reliability: Completes scoped work with quality and predictable follow-through; manages time effectively.
• Communication: Communicates progress, risks, and results clearly; asks good questions and seeks guidance when needed.
• Technical learning mindset: Learns quickly, integrates feedback, and actively develops deeper expertise in at least one area.
• Evaluation awareness: Understands the importance of credible evidence; contributes to test design and results interpretation.
• Collaboration: Works effectively with researchers and engineers; contributes constructively to discussions that shape tasking and delegation.

Desired Experience

• Coursework or applied experience in machine learning, statistics, or data-driven software systems.
• Experience implementing model training/inference, data pipelines, or evaluation tooling for CV and/or LLMs.
• Familiarity with reproducible research/software practices (version control, experiment logging, container basics).
• Experience delivering prototypes, demos, or technical artifacts for stakeholders (internal or external).
• Interest in DoW/government mission applications and working within operational constraints.

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 will be subject to a background investigation and must be able to obtain and maintain a Department of War security clearance.
• You must be able and willing to work onsite 5 days per week at the SEI's facility in Pittsburgh, PA.

Location: Pittsburgh, PA

Job Function: Software/Applications Development/Engineering

Position Type: Staff - Regular

Full time/Part time: Full time

Pay Basis: Salary