Machine Learning Engineer

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
• Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
• Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
• Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
• Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
• Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
• Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
• Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
• Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
• Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

AI/ML Engineer

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 & Integrate: Design and implement full-stack features that utilize LLMs to solve user problems.
• Streaming Interfaces: Build low-latency interfaces that stream text to the client.
• Roadmap Ownership: Leading a team of developers to build new features.
• Write robust, high-performance Go code to orchestrate AI services.
• Design and implement RESTful and gRPC APIs.
• Collaborate on integrating Kafka and Redis to build asynchronous message processing pipelines.
• Use AI coding assistants to accelerate code generation, testing, and documentation.
• Critically evaluate and debug AI-generated code.
• Refine system prompts and write automated evaluations for AI performance.

Staff AI Engineer

• 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

• 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

• 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

• Own the entire ML lifecycle: creating POCs, building production-ready systems, deployment, monitoring and maintenance.
• Create scalable, production-ready ML solutions, and optimize the performance and scalability of machine learning models.
• Design and conduct offline experiments to validate the impact of algorithmic changes on key business metrics.
• Engage in continuous learning and development.

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