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

• Build the AI Troubleshooting Engine That Powers the Future of Heavy Machinery
• Own customer outcomes end-to-end, from first discovery workshop to production rollout
• Work directly with machinery manufacturers (and their distributors/service teams)
• Turn messy, real-world troubleshooting into a continuously improving AI system
• Shape product with real customers, help define the intelligence layer for the heavy-equipment ecosystem
• Influence architecture, reliability, AI evaluation + monitoring
• Stabilize MVP for production-ready troubleshooting assistant
• Establish early engineering culture, onboarding docs, architecture patterns, and delivery cadence

Founding Applied AI Engineer

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

• 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

• 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.

See all jobs on JobTailor