AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

AI Engineer

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches
• Collaborate cross-functionally with product/business stakeholders to translate workflows into well-scoped deliverables

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

Machine Learning Engineer

• Build and ship production-grade AI systems used daily by real customers
• Design multimodal pipelines for drawings, specs, and construction documents
• Build agentic workflows automating preconstruction tasks: Document intake, Extraction, Takeoff support, Bid preparation, QA workflows
• Build and improve: Retrieval systems (embeddings, reranking, chunking), Evaluation systems, Orchestration layers
• Optimize model quality, latency, reliability, and cost efficiency
• Prototype and iterate quickly on customer-facing AI features
• Own systems end-to-end (experiment → deploy → monitor → improve)
• Help shape engineering culture and long-term technical direction

Founding AI/ML Engineer

• Collaborate with a cross-functional group of technologists, product managers, and archivists to shape the future of ethical AI in journalism.
• Use generative AI to surface archival content for contemporary use.
• Partner with RAD to design and prototype workflows that transform unstructured archival data into structured, discoverable assets.
• Establish standards for the ethical use of artificial intelligence with NPR's archival content.
• Prepare digital collections for machine learning and research approaches that pair AI with human editorial judgment and metadata to find new insights from the archive.
• Identify opportunities and build functional prototypes that enable reporters and producers to query historical audio for contemporary reporting.
• Assist in the development of automated workflows to transform unstructured legacy media into a searchable, AI-indexed library.
• Analyze historical data troves to surface evergreen content opportunities and extract narrative leads that support contemporary reporting.
• Evaluate AI outputs and discovery tools for accuracy and bias to ensure technical prototypes maintain archival integrity.

AI Research Intern, Ethics and Readiness

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS
• Engineer robust runtime behavior
• Enable workflow automation and integration
• Create and maintain technical documentation
• Collaborate cross-functionally with product/business stakeholders
• Other duties as assigned

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks, circuit breakers, and guardrails
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches, and operational runbooks
• Collaborate cross-functionally with product/business stakeholders to validate outcomes against measurable success criteria
• Other duties as assigned.

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Advise and make recommendations to senior IT and business leaders on areas for service simplification.
• Define and maintain system architecture and technology stack.
• Collaborate with product managers, designers, business intelligence engineers, and other stakeholders to make technical decisions, define technical requirements, and create development roadmaps in service of business goals.
• Conduct code reviews, provide constructive feedback, and promote continuous improvement in code quality and development processes.
• Mentor engineers and enforce best practices.
• Contribute hands-on across the stack.
• Collaborate with DevOps for CI/CD and cloud deployments.
• Participate in hiring and onboarding new team members.

Principal AI Engineer

• Design and implement AI solutions that operate across multi-cloud and hybrid environments, including Azure, Google Cloud Platform (GCP), and AWS.
• Evaluate and recommend hosting, infrastructure, and deployment architectures based on mission requirements, security posture, scalability, portability, and long-term maintainability — not tied to a single cloud provider.
• Architect AI/ML platforms and applications using cloud-native and containerized technologies that minimize vendor lock-in and maximize interoperability.
• Build and modernize web applications using modern full-stack frameworks and AI assisted development tooling to accelerate delivery and improve developer productivity.
• Develop sophisticated conversational AI and knowledge management solutions that enable users to securely interact with enterprise data using natural language.
• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, semantic search, knowledge graphs, and document processing pipelines.
• Lead the design of secure cloud and on-premises AI infrastructure solutions that meet Federal Government and internal compliance requirements.
• Define architectural design decisions affecting infrastructure selection, orchestration, model hosting, observability, scalability, and system behavior for AI enabled solutions.
• Analyze client requirements and emerging AI technology trends to recommend scalable, compliant, and future-proof architectures.
• Participate in design thinking and solution engineering activities to solve complex mission and operational challenges.
• Lead whiteboarding and technical strategy sessions with clients, partners, and internal teams.
• Communicate findings and recommendations to both technical and non-technical stakeholders, translating complex AI and infrastructure concepts into actionable guidance.
• Ensure infrastructure and application architectures support secure software delivery practices including CI/CD, Infrastructure as Code (IaC), automated testing, and model lifecycle management.

• Set the technical direction for the agentic data foundations
• Design, prototype, and ship systems for agent interactions
• Lead complex, multi-team initiatives across Agentic AI and Platform teams
• Mentor Senior and Staff engineers

Principal Machine Learning Engineer

• Architect and deliver end-to-end AI solutions — RAG pipelines, agentic workflows, multi-modal applications, that are modular, interoperable, and designed for portability across cloud providers and on-premises environments.
• Lead LLM-agnostic design decisions: evaluate and integrate models from OpenAI, Anthropic, Meta (Llama), Mistral, Cohere, and open-source alternatives based on mission fit, not vendor preference.
• Design and implement AI agents and multi-agent orchestration using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or custom implementations, with clear separation of concerns to enable future platform migration.
• Build and optimize retrieval systems using vector databases (Pinecone, Weaviate, pgvector, OpenSearch) and hybrid search strategies not tied to a single managed service.
• Own the full AI lifecycle: data ingestion, embedding strategy, chunking, prompt engineering, evaluation, fine-tuning (LoRA/PEFT), and continuous monitoring.
• Design infrastructure using IaC (Terraform, Pulumi) with multi-cloud support across Azure (OpenAI, AI Search, AKS), AWS (Bedrock, SageMaker), and GCP (Vertex AI) — enabling lift-and-shift without architectural rework.
• Apply semantic chunking, knowledge graph integration, and structured data fusion to improve retrieval fidelity in complex government data environments.
• Implement robust evaluation frameworks (RAGAS, LLM-as-judge, human-in-the-loop review) to measure and improve AI system quality continuously.
• Engage clients directly whiteboard sessions, solution design reviews, and stakeholder presentations translating complex AI architecture into clear mission outcomes.
• Ensure all AI solutions comply with federal standards (FedRAMP, FISMA, NIST AI RMF, NIST 800-53) and apply responsible AI practices including bias detection and model explainability.

• Analyze detection data including customer reports to determine which aspects of the machine learning models can be improved
• Perform tasks to enable better-management of false positive detections
• Analyze binary files to determine their legitimacy
• Review current product detections to ensure they are performing to the company standard
• Address internal questions and concerns regarding customer threat detections

Machine Learning Detection Engineer

• Tackle real business challenges, collaborate across teams, and contribute ideas that drive results
• Build meaningful relationships through social events, peer engagement, and shared experiences
• Receive guidance from experienced professionals through our dedicated mentorship program
• Participate in a custom onboarding experience and curated learning series

AI Research Intern

• Train, test, deploy, and maintain models that learn from data
• Own and define mission-critical solutions by implementing AI that enables faster mission success
• Collaborate with data engineers, data scientists, solutions architects, and product owners

• Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen
• Develop and deploy multi-agent systems using Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols to facilitate communication, tool usage, and collaborative task solving
• Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention
• Fine-tune SLMs for specific domains and optimize them for edge device performance, including ONNX, GGML, or Ollama
• Develop evaluation frameworks to test agent reliability, safety, and performance, moving from prototype to production, including ReAct loops and human-in-the-loop

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications.
• Choose to work in areas like query understanding, search relevance and ranking, generative recommendations, LLM evaluation, and more.

Machine Learning Engineer, PhD Intern

• Own strategy and evolution of platform capabilities that enable AI assistants, workflows, and integrations.
• Define how internal AI systems and external partners connect to FUB data through well-designed tools and integration patterns.
• Shape intelligence capabilities that help AI better understand agent and lead activity.
• Lead product direction for memory capabilities that make AI experiences more personalized over time.
• Partner closely with engineering, AI/ML, design, and product peers to assess tradeoffs and define solutions.
• Help identify and prioritize highest-value platform opportunities based on product needs and long-term strategy.
• Drive alignment across teams by communicating roadmap priorities and progress clearly to stakeholders and leadership.
• Contribute to long-term vision for how AI platform capabilities show up in product experiences across web, mobile, automation, and assistant surfaces.

See all jobs on JobTailor

AI Research Scientist, Applied AI

AI Engineer

M365 Automation and AI Developer

Founding AI/ML Engineer

AI Research Intern, Ethics and Readiness

AI Engineer

AI Engineer

QuantAI Engineer – Trading, Investment & Optimization

Senior Manager, AI Lead – Semantic Layer

Principal AI Engineer

AI Engineer

Principal Machine Learning Engineer

AI Engineer

Machine Learning Detection Engineer

AI Research Intern

Machine Learning Engineer

AI Engineer

Senior AI Engineer, Agentic Evaluation – V&V

Machine Learning Engineer, PhD Intern

Senior Product Manager, AI Platform – Ecosystem