Senior Machine Learning Research Scientist - Frontier Lab

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, Skill

Benefits

Additional Information

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 Do W 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.

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Company Intel

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