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

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

AI 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

• As a Principal AI Engineer at Rula, you will own the AI foundation that determines how generative AI shows up across mental healthcare.
• You will operate where architecture, applied research, and real-world clinical constraints meet, designing durable, production-grade systems that power patient engagement and provider workflows at scale.
• This is a deeply hands-on role where you will act as a technical multiplier, architecting core platform primitives (RAG, agentic loops) while setting the standard for technical excellence and safety in a regulated environment.

Principal AI Engineer

• 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

• Work with business and operational teams to understand the problem, the operational and system constraints, the available data, and decision points.
• Define how opportunities fit within the long-term evolution of the systems.
• Based on the problem and the available data, determine how we can measure success and monitor system outcomes/dynamics.
• Define and drive the evolution of the solution, identifying points of high leverage, and phases/evolution of the system.
• Evolve plans and direction as we iteratively gather feedback and learn about solution effectiveness.
• Collaborate with cross-functional leaders to identify opportunities, communicate and evaluate solutions, and gather feedback.
• Mentor of data scientists and analysts, fostering a collaborative and high-performing environment.
• Provide technical guidance, set standards, and ensure the team uses appropriate tools and methodologies.
• Stay current with emerging trends in data science, AI, and big data technologies to drive innovation and maintain a competitive advantage.

Principal Machine Learning Revenue Architect

• 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

• At Rula, this role owns the AI foundation that determines how, and how well, AI shows up across mental healthcare.
• The focus isn’t on prototypes or isolated features, but on building durable, production-grade generative AI systems that directly power patient engagement, provider workflows, and clinical operations at scale.
• You’ll operate where architecture, applied research, and real-world constraints meet.
• The work includes designing core AI platforms, setting technical standards for safety, reliability, and performance, and making principal decisions about how advanced AI is introduced into a regulated, high-stakes care environment.
• This is a deeply hands-on role with real technical ownership. You’ll define system architecture, unblock complex engineering problems, and act as a technical multiplier for other engineers working across product surfaces.
• Success here means creating foundations that others can build on confidently and that hold up under scale, scrutiny, and real patient impact.

Senior Staff AI Engineer

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

Senior Software Engineer II, ML/AI Platform

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

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