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

• Design, build, and operate scalable ML and data pipelines for spatial-temporal and sensor-driven datasets.
• Operationalize data science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
• Implement and maintain containerized ML workloads in cloud-native environments.
• Integrate model outputs into downstream serving systems and analytical platforms to support web-based applications and operational decision-making.
• Develop and maintain CI/CD pipelines for ML and data services.
• Collaborate closely with data scientists to operationalize experimental models into reproducible, observable, and scalable production systems.
• Take ownership of MLOps practices within an applied research team, bringing structure, repeatability, and best practices to evolving environments.

MLOps Engineer

AI Engineer

• Lead and scale the Forward Deployed AI Engineering team across its two functions: customer-embedded engineering and AI & Data Science
• Own the team's mandate: embed in strategic customer accounts to discover problems, build high-grade solutions, and produce cross-customer intelligence that elevates the entire platform
• Define the engagement model for FDE work — how engineers are assigned to accounts, how engagements are scoped, and how solutions are documented and productized
• Oversee the AI & Data Science function — ensuring cross-customer telemetry, threat intelligence, and detection optimization work is translated into tools that the SE&A team can operate
• Partner with Sales to identify strategic accounts that are candidates for FDE engagement
• Partner with the SE&A and Rapid Deployment teams to ensure FDE-built solutions flow back into the broader delivery model
• Serve as an executive-level presence with strategic customers — participating in account reviews and helping define the roadmap for embedded engagements
• Evaluate new AI technologies, data science tooling, and engineering approaches that can improve team output and customer outcomes
• Hire, develop, and retain engineers across both the FDE and AI & DS functions

Director of Forward Deployed AI Engineering

• 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

• 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

• Build and manage the complete machine learning and generative AI lifecycle, including research, design, experimentation, development, deployment, monitoring, and maintenance.
• Design, develop, and deploy LLM-based and generative AI models to power scalable and intelligent enterprise applications.
• Architect, optimize, and maintain retrieval-augmented generation (RAG), prompt orchestration, and contextual reasoning pipelines to support diverse AI use cases.
• Implement scalable MLOps pipelines for model deployment, performance monitoring, and continuous improvement.
• Conduct fine-tuning, alignment, and evaluation of LLMs and multimodal models to ensure reliability, efficiency, and fairness.
• Collaborate with data science, engineering, and product teams to translate business needs into generative AI-driven solutions.
• Perform benchmarking, evaluation, and optimization of generative models to improve accuracy, latency, and cost efficiency.
• Research and apply emerging techniques in transformer architectures, multimodal learning, and generative modeling to drive innovation and enhance enterprise capabilities.
• Ensure secure, ethical, and responsible AI deployment, embedding fairness, transparency, and compliance throughout the model lifecycle.
• Mentor and guide team members on generative AI frameworks, best practices, and experimentation methodologies.

Senior Engineer, Machine Learning Engineering

• Design, develop, and deploy AI agents and machine learning models to solve complex HR challenges, such as employee sentiment analysis, talent acquisition optimization, and workforce planning.
• Partner with HR stakeholders and data analysts to identify business needs and translate them into technical requirements for AI solutions.
• Conduct exploratory data analysis to identify trends, patterns, and insights that can inform the development of AI models.
• Monitor, evaluate, and fine-tune deployed AI models to ensure optimal performance and accuracy.
• Stay up-to-date with the latest advancements in AI, machine learning, and data engineering to continuously improve our capabilities.
• Design, build, and maintain robust data pipelines to ingest, transform, and store HR data from various sources (e.g., APIs, databases, flat files).
• Optimize data architecture and databases to improve performance and scalability.
• Collaborate with IT and other data professionals to ensure the seamless integration of data solutions and to maintain a scalable data infrastructure.
• Adhere to data governance and security best practices to protect sensitive HR information.

HR AI/ML Engineer II

• Lead the development + implementation of real-time feature detection and anomaly detection models
• Generate data characteristic requirements for real-time data processing pipelines
• Prepare technical documentation, reports, and specifications
• Collaborate with cross-functional teams including project managers, technicians, and other engineers
• Perform testing, troubleshooting, and quality assurance on systems or products
• Ensure compliance with safety regulations, industry standards, and company policies

Senior Machine Learning Engineer

• 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, 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

LLM Engineer

• Define and execute long-term vision for cloud infrastructure
• Partner directly with Engineering to architect elastic and efficient systems
• Build delightful user experience by understanding pain points of developers
• Establish and own metrics for adoption, performance, and cost-efficiency
• Act as lead evangelist, building consensus across groups
• Craft and execute rollout strategies for platform usage

Group Product Manager – Cloud & AI Infrastructure

• Translate Data Science Algorithms and Methods in R&D stage to stable, durable, and extensible software
• Provide thought leadership on best algorithm development practices and hosting technologies
• Engineer fully featured and real-time modeling pipelines that leverage the best of containerization, data storage, streaming, and MPP technologies
• Engineer and maintain ecosystem of modeling micro-services utilizing best of on-prem and cloud-based elastic compute
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure in collaboration with stakeholders
• Design and engineer high performance computing platforms and algorithms for a wide variety of data
• Work with real time data sources and have a knack for identifying scalable data access patterns
• Work closely with R&D data science teams to productionize successful models in an on-demand environment

See all jobs on JobTailor

AI Research Scientist, Applied AI

MLOps Engineer

Director of Forward Deployed AI Engineering

Senior Manager, AI Lead – Semantic Layer

Senior Software AI Engineer

Senior Engineer, Machine Learning Engineering

HR AI/ML Engineer II

Senior Machine Learning Engineer

AI Engineer – III

AI Engineer

Senior AI Engineer, Agentic Evaluation – V&V

Group Product Manager – Cloud & AI Infrastructure

Senior Machine Learning Engineer