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, build, and maintain integrations between the Anthropic Claude platform and enterprise systems
• Develop and manage MCP server configurations
• Build and optimize prompt pipelines, agentic workflows, and automations
• Troubleshoot complex technical issues across the AI stack
• Write clean, well-documented code
• Test and iterate on AI workflows

Manager, AI Engineer

• Architect and lead the development of agentic AI systems that automate and augment finance workflows.
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns.
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices.
• Develop and integrate AI services into internal applications and workflows.

Principal Applied AI Engineer, Finance

• 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 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

• Position SummaryThe Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data preprocessing, model training, and fine-tuning using large-scale healthcare datasets. This role requires a strong understanding of Large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data.
• Key Responsibilities
• - Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling missing values, outlier detection, feature engineering, and data normalization. Identify, collect, and curate relevant, industry-specific datasets for model retraining. Format data appropriately for the chosen LLM and training pipeline
• - Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software. Train and fine-tune pre-trained LLMs on the custom dataset to achieve specific goals. Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance. Integration of structured + unstructured data (multi-modal/multi-input models)
• - Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.
• - Pipeline Development: Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.
• - Collaboration: Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.
• - Research & Development: Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions.
• - Documentation: Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.

Senior Machine Learning 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

• Architect and lead the development of agentic AI systems that automate and augment finance workflows (e.g., forecasting, reporting, and decision support).
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns (e.g., RAG, model chaining, dynamic prompting).
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Design fault-tolerant, observable agent systems with clear failure modes and recovery strategies.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Translate complex predictive insights into actionable recommendations for finance and business leaders.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices (modular design, APIs, CI/CD, testing).
• Lead end-to-end development from concept to production, ensuring robustness, scalability, and maintainability.
• Develop and integrate AI services into internal applications and workflows, including light front-end/back-end components where needed.
• Drive adoption of modern tooling (e.g., containerization, orchestration, cloud-native architectures).
• Establish and enforce MLOps best practices for deployment, monitoring, retraining, and governance of AI systems.
• Ensure systems meet enterprise standards for security, compliance (e.g., SOX), and auditability.
• Develop advanced feature engineering strategies capturing behavioral, financial, and temporal signals.
• Set technical direction for AI/ML initiatives across the finance organization.
• Lead complex, cross-functional projects and mentor other data specialists.
• Work alongside stakeholders across finance, IT, and product to adopt AI-driven solutions.
• Contribute to long-term AI strategy, identifying opportunities to drive efficiency and innovation.

See all jobs on JobTailor

AI Research Scientist, Applied AI

Senior Machine Learning Engineer II, Search & Recommendations Ranking

Manager, AI Engineer

Principal Applied AI Engineer, Finance

AI Engineer

Senior AI Engineer, Agentic Evaluation – V&V

Machine Learning Engineer, PhD Intern

Senior Machine Learning Engineer

Senior Software Engineer II, ML/AI Platform

Principal Applied AI Engineer, Finance