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

• Responsible for architecting solutions achieving customer business outcomes
• Develop and articulate compelling, accurate, and relevant proposals
• Provide technical expertise to sales teams and customers
• Develop customer proposals and critically review them
• Quantify the impact of business problems and position business value
• Communicate how the solution value propositions address customer business needs
• Drive the Account Business Planning process leveraging knowledge of industry trends and customer’s technical environment

AI Architect – Pre-Sales

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

• Lead the design, development, and deployment of production AI systems end-to-end
• Contribute hands-on to the codebase and set a high bar for engineering quality
• Establish scalable patterns for evaluation, deployment, and system reliability
• Conduct technical design reviews and guide architectural decisions
• Mentor and develop engineers, providing regular coaching and technical guidance
• Support hiring efforts, including candidate evaluation and onboarding
• Partner with cross-functional stakeholders to align priorities and deliver impactful solutions
• Help translate experimentation into stable, production-ready systems

Full Stack AI Engineer

• Design and analyze neuromorphic AI algorithms for robotics, signal processing, control, and learning on edge platforms
• Develop research-quality software artifacts including APIs, kernels, and benchmarking tools that enable collaborative innovation
• Prototype and evaluate approaches under realistic constraints including quantization, sparsity, memory footprint, latency, and energy optimization
• Collaborate with hardware and software engineering teams to translate algorithmic requirements into hardware specifications
• Build experimental pipelines and benchmarks for target use cases with comprehensive baseline and ablation studies
• Communicate results through technical documentation, customer prototypes, and publications in leading conferences and journals
• Contribute to technical thought leadership in neuromorphic computing and present findings to internal and external stakeholders

Neuromorphic/AI Research Scientist

• This candidate will be configuring data pipelines, establishing data ingestion processes, and implementing predictive modeling components that analyze five (or more) years of historical data to generate time- and location-based risk forecasts.
• Role includes integration with existing systems and data to ensure seamless data flow and usability within current operational workflows.
• Experience with all AI models to validate data integrity, model performance, and system reliability.
• Assessing prediction accuracy, ensuring alignment between forecasted and observed patterns, and verifying that outputs are operationally meaningful for command staff and field leadership.
• Experience within STRIDE platform.
• Build machine learning and deep learning models (e.g., neural networks, LLMs) to address specific business problems.
• Fine-tune AI models to ensure high performance, scalability, and efficiency in cloud environments.

Senior AI Engineer

• 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

• Develop and deploy AI models across the insurance value chain, leveraging both supervised and unsupervised learning techniques.
• Design, implement, and optimize Retrieval-Augmented Generation (RAG) pipelines for AI-driven decision support.
• Utilize Vector Databases and Knowledge Graphs to enhance AI applications in underwriting, claims processing, and customer engagement.
• Develop data pipelines for ingestion, transformation, and storage to support AI/ML workloads.
• Design and implement scalable solutions using cloud-based AI platforms such as Azure Machine Learning, Databricks.
• Implement MLOps best practices, including CI/CD for model training, validation, deployment, and monitoring.
• Develop generative AI models for personalized customer experiences and automation of complex decision-making processes.
• Apply natural language processing (NLP) techniques to analyze and extract insights from unstructured data sources.
• Optimize AI models for performance, scalability, and reliability in enterprise environments.
• Conduct architecture design reviews and performance tuning for AI/ML applications
• Work cross-functionally with business and technology teams to identify AI-driven opportunities and define strategies.
• Ensure compliance with ethical AI principles, model governance, and data privacy regulations.
• Demonstrate behaviors consistent with PEMCO's policies, values, code of ethics and business conduct.
• Authentically support the PEMCO Brand and constantly are on the lookout for top talent to join us to achieve our Mission to Worry Less and Live More.
• Other duties as assigned.

Senior Data and AI Scientist

• Design, build, and deploy generative and agentic AI solutions that automate, optimize, and transform tools supporting Medtronic’s global operations
• Collaborate closely with cross-functional partners to implement intelligent automation, architect AI-powered workflows, and develop scalable, robust APIs and platforms that drive operational efficiency and enhance staff experience across global facilities
• Support Medtronic’s strategic objectives by leveraging advanced AI / Machine Learning (ML) frameworks and modern development practices to deliver measurable business impact
• Ensure contributions align with the company's mission to alleviate pain, restore health, and extend life

Principal Artificial Intelligence Engineer

• Own and ship internal automation roadmap
• Build production systems on top of AI models
• Collaborate directly with engineering and delivery leadership
• Turn documented automation workflows into shipped, working systems

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Manage and develop the overall Artificial Intelligence roadmap for the company
• Provide leadership, management and direction companywide on artificial intelligence initiatives
• Develop market strategies for the company
• Lead Artificial Intelligence team by making strategic decisions to resolve robotics process automation, natural language processing, and machine learning
• Understand the options in the market and educating companywide on these opportunities
• Collaborate and influence all levels of professionals
• Manage allocation of people and financial resources for Artificial Intelligence Solutions
• Develop and guide a culture of talent development to meet business objectives and strategy

AI Solutions Director

• Own the end-to-end administration of the Association’s primary AI platform and Microsoft Copilot
• Manage user provisioning and deprovisioning, SSO configuration, permission tiers, and seat allocation across standard and Pro/Max license tiers
• Ensure platform configurations meet enterprise security standards and Responsible AI guidelines
• Design, build, and maintain integrations between AI platforms and core enterprise systems
• Partner with Enterprise Risk to ensure all AI platform configurations meet data security policies
• Track platform consumption patterns and generate usage analytics
• Serve as the primary technical point of contact with AI platform vendors
• Lead technical due diligence when new AI tools are proposed
• Own the build and maintenance of internal CoE tooling
• Forecast platform capacity needs as adoption grows

Director of AI Platform Operations

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

Machine Learning Engineer

• Develop and implement advanced search solutions
• Lead the development and optimization of search algorithms and systems utilizing technologies such as Elastic/OpenSearch
• Fine-tune search relevance using machine learning techniques
• Explore and implement cutting-edge technologies including Agentic Solutions, RAG, and LLMs
• Analyze search data and user behavior to inform optimization strategies
• Collaborate with cross-functional teams to understand search requirements and deliver results
• Develop and evaluate analytical models to investigate search-related problems
• Utilize strong programming skills in Python to examine and interpret search data

Senior Software Development Engineer – Search Machine Learning

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Design end-to-end AI systems for customer experience use cases.
• Architect reliable, production-ready AI solutions that go beyond prompt design, combining LLMs, deterministic workflows, tools, and orchestration layers.
• Define how AI interacts across the full journey (self-service, agent copilot, journey management, and back-office automation), including fallback strategies, human handoff, and failure handling.
• Optimize retrieval-augmented generation (RAG) and knowledge architectures to extend Genesys solutions where required.
• Design scalable knowledge and retrieval strategies that ground AI responses in enterprise data.
• Develop approaches for content structuring, chunking, embedding, and ranking to ensure accuracy, relevance, and freshness.
• Partner with customers to align AI outputs with trusted knowledge sources while balancing performance, latency, and governance requirements.
• Establish AI evaluation frameworks and quality measurement strategies.
• Define how success is measured for AI-driven experiences, including accuracy, containment, customer satisfaction, and business impact.
• Create test sets, evaluation methodologies, and feedback loops to continuously improve performance.
• Translate technical quality metrics into business-relevant outcomes to support customer decision-making and adoption.
• Engineer contextual AI experiences that leverage real-time data and conversation state.
• Design how AI systems incorporate dynamic context such as customer data, interaction history, and external signals.
• Optimize context management and prompt structure to maximize relevance while managing token limits and response quality.
• Ensure AI interactions remain coherent, personalized, and aligned across channels and touchpoints.
• Design for scalability, latency, and cost efficiency in enterprise environments.
• Evaluate and optimize AI solutions for real-world constraints, including response time (especially for voice), throughput, and cost at scale.
• Make informed tradeoffs across model selection, caching strategies, and architecture patterns to deliver performant and economically viable solutions.
• Ensure designs meet enterprise expectations for reliability and responsiveness.
• Leverage expert-level knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support pre-sales activities such as technical discovery, solution design, product demonstrations, sandbox/trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, and success criteria, and to highlight the differentiated value of Genesys AI.

Senior AI Architect

• 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.

See all jobs on JobTailor

AI Research Scientist, Applied AI

AI Architect – Pre-Sales

Principal AI Engineer, HR Automation

Full Stack AI Engineer

Neuromorphic/AI Research Scientist

Senior AI Engineer

Senior Manager, AI Lead – Semantic Layer

Senior Data and AI Scientist

Principal Artificial Intelligence Engineer

AI Engineer

Senior Software AI Engineer

AI Solutions Director

Director of AI Platform Operations

AI Engineer – III

Senior Software Development Engineer – Search Machine Learning

AI Engineer

Senior AI Architect

Senior AI Engineer, Agentic Evaluation – V&V