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

Machine Learning Engineer

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

AI Engineer

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.

Software Engineer 2 – AI Platform

• Support the Product Manager in refining and executing the agentic AI digital media platform strategy
• Own end-to-end delivery of AI-powered features from concept to production
• Design new intuitive user experiences powered by intelligent automation
• Partner with engineering to define AI system behavior and interaction patterns
• Translate new business workflows into scalable product functionality
• Improve evaluation metrics for AI-driven features (accuracy, reliability, latency, ROI impact)
• Lead experimentation to improve AI-driven feature performance
• Improve human-in-the-loop review and escalation workflows
• Collaborate with engineering to implement APIs and integrations
• Support stakeholder analysis to understand user behavior and requirements
• Improve user testing programs to validate assumptions
• Contribute to product roadmap definition and own detailed roadmap execution and prioritization
• Translate business requirements into product specifications and user stories
• Manage backlog, sprint planning, and delivery cadence
• Prioritize initiatives based on ROI and technical feasibility
• Collaborate with sales, marketing, and customer success for launches
• Monitor product performance and drive continuous improvement

Product Owner – Agentic AI Platform

• 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

• Develop actionable insights and recommendations in support of business objectives using in-house and external data sources
• Collaborate with business and data engineering professionals for timely and reliable delivery of data solutions
• Leverage deep expertise in data engineering, data science, advanced analytics, and insurance industry data to aid business partners in risk management
• Drive efficiencies and improve customer experience
• Partner with business stakeholders to scope, structure, and drive analytical projects from requirements through delivery
• Present findings and recommendations clearly to both technical and non-technical audiences
• Source, validate, and integrate structured and unstructured data across platforms
• Conduct exploratory data analysis to reveal data quality issues and identify opportunities
• Analyze business trends, time series patterns, model performance, and causal relationships using advanced statistical and machine learning / deep learning methods
• Design, build, and deploy machine learning / deep learning models — including NLP, computer vision, and LLM-based solutions
• Create visualizations, dashboards, and reports that communicate analytical and model results to support decision-making across the organization
• Own and maintain recurring analytical, reporting, and model-monitoring processes
• Document data pipelines, model architecture, analytical frameworks, and deliverables for knowledge sharing across technical and non-technical teams

Machine Learning Scientist

• Design, develop, and maintain both front-end interfaces and backend services with AI capabilities
• Create and manage RESTful APIs and integrate AI services such as LLMs, embeddings, and document parsing tools
• Extract, transform, and analyze large datasets to enhance product features and user experience
• Implement highly optimized machine learning pipelines with quantized models for legacy hardware
• Ensure high code quality through rigorous testing, code reviews, and debugging
• Proactively solve technical issues and contribute to all phases of the development lifecycle
• Stay updated with emerging technologies and incorporate them into our operations where beneficial

Senior Agentic AI Engineer

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.
• Implement evaluation frameworks, observability, and guardrails to ensure correctness, safety, and compliance of AI-driven systems.
• Design solutions for context management, memory, and retrieval-augmented generation (RAG) to enhance agent effectiveness.

• 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

• Design and implement AI-powered customer support solutions (chatbots, virtual agents, agent assist tools)
• Build and maintain systems for automating ticket triage, classification, routing, and response generation
• Develop retrieval-augmented generation (RAG) systems using internal knowledge bases and documentation
• Integrate AI into support platforms (e.g., Salesforce Service Cloud, etc.)
• Analyze workflow data (tickets, chats, call transcripts) to identify automation opportunities and insights
• Optimize AI systems for accuracy, latency, cost, and customer satisfaction
• Establish evaluation frameworks (e.g., response quality, resolution rate, deflection rate)
• Collaborate with services and support teams to continuously refine workflows and ensure practical usability
• Implement guardrails and monitoring to ensure safe, reliable, and compliant AI behavior

AI Developer – Customer Services and Support

• 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

• 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

• Build and scale the AI Agent platform — enabling high-volume, real-time conversational workflows that deliver accuracy and low latency at scale.
• Design and implement APIs, services, and infrastructure that power multi-turn, cross-channel customer interactions.
• Own the full lifecycle: architecture, implementation, deployment, and ongoing reliability.
• Prototype rapidly, iterate with live interaction data, and continuously improve system performance and user experience.
• Implement observability, monitoring, and operational best practices to ensure reliability in production, focusing on agent accuracy and latency.
• Collaborate with engineers, product managers, and AI/ML scientists to deliver end-to-end features that power critical business outcomes.

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AI Research Scientist, Applied AI

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Product Owner – Agentic AI Platform

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Machine Learning Scientist

Senior Agentic AI Engineer

Software Engineer 2 – AI Platform

AI Engineer – III

AI Developer – Customer Services and Support

AI Engineer

Senior AI Engineer, Agentic Evaluation – V&V

Product Owner – Agentic AI Platform

Senior AI Engineer