Senior Machine Learning Engineer Mission Innovation Lab

Senior Machine Learning Engineer(FullStack / AppliedResearch) Mission Innovation Lab

At the SEI AI Division we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI technologies and systems. We currently lead a community-wide movement to mature the discipline of AI Engineering for Defense and National Security.

As our government customers adopt AI and machine learning toprovideleap-ahead mission capabilities we

• build real-world mission-scale AI capabilities through solving practical engineering problems

• discover and define the processes practices and tools to support operationalizing AI for robust secure scalable and human-centered mission capabilities

• prepare our customers to be ready for the unique challenges of adopting deploying using andmaintainingAI capabilities

• identifyand investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape

Are you creative curious energetic collaborative technology-focused and hard-working Are you interested in making a difference by bringing innovation to government organizations and beyond Apply to join our team.

Overview

As aMachineLearning Engineer who can take research ideas from concept to prototype you willlead independent appliedresearch projects for defensefocused missions.

The ideal candidate is comfortable across the full stack (data pipelines model development API services and secure deployment) and eager to explore novel AIandML theory while delivering missionscale capabilities.

The Mission Innovation Lab within the SEIs AI Divisionworks with the defense and national security community to translatetherecentlypossible in AI into reliable mission and warfighting capabilities.

Key Responsibilities

• Design implement and evaluate stateoftheart ML models (computervision NLP planning etc.) using frameworks such as TensorFlowPyTorch Torch or Caffe.

• Build andmaintainrobust data pipelines ETL processes and backend services in Python C/C and Java.

• Lead rapidprototyping efforts translate research results into operational prototypes and test for performance robustness and security.

• Define and refineDevSecOpspractices for ML (model registries containerized deployment continuous integration/continuous delivery security scanning).

• Mentor junior team members collaborate with researchers government customers and other engineers and contribute to technical strategy for the lab.

Required Qualifications

• B.S. in Computer Science Electrical Engineering Statistics or related field with10years ofexperience;OR M.S. with 8years;OR Ph.D. with 5years of relevant experience.

• Ability to obtain andmaintainan active Department ofWarsecurity clearance.
  

• You must be able and willing to work onsite 5 days per week at an SEI office in either Pittsburgh PA or Arlington VA.

• Strong experience inone or more programming language such asPython C/C and Java; comfortabledevelopingproductiongrade code and APIs.

• Solid understanding ofMLtheory statistical learning and common algorithms.

• Handson experience with TensorFlowPyTorch Torch Caffe or similar deeplearning libraries.

• Familiarity with CI/CD pipelines container orchestration (Docker/Kubernetes) model versioning and securityfocused tooling.

Desired Experience

• Proventrack recordofindependentappliedresearch projects that resulted in demonstrable prototypes or operational capabilities.

• Publications or opensource contributions inAIandML especially in adversarial or robust ML.

• Experience working on defense or other highimpact government programs.

• Ability to quickly learn emerging AIandML technologies and translate them into missionrelevant solutions.

Knowledge Skills & Abilities

• Deep technical knowledge of modern ML methods and ability to extend them to novel domains.

• Excellent written and verbal communicationskills; capable of presenting complex ideas to technical and nontechnical audiences.

• Strong collaborative mindset; experience working in interdisciplinary teams and mentoring peers.

• High degree of scientific curiosity and a proactive selfdirected work style.