• Collaborate with senior engineers and mentors to support the development and evaluation of AI-powered educational tools • Contribute to research-backed initiatives focused on improving AI system behavior, including identifying and measuring issues such as model bias and misalignment (e.g., sycophancy) • Assist in designing and executing experiments to evaluate AI performance in real-world learning scenarios • Translate evaluation findings into actionable insights that improve AI system quality and reliability • Support the mapping of AI evaluation work to established governance or responsible AI frameworks • Participate in code reviews, team discussions, and knowledge-sharing sessions • Take ownership of defined tasks while demonstrating initiative and creative problem-solving

Job Title: AI/ML Engineer Location: Dayton, OH and Remote Employment Type: Full-Time Clearance requirements: TS/SCI About the Role Rackner is seeking a highly skilled AI/ML Engineer to design, develop, and deploy advanced machine learning solutions that support mission-critical systems. This role will focus on building scalable models, developing training pipelines, and collaborating with cross-functional teams to deliver impactful AI-driven solutions. Key Responsibilities - Design, develop, and implement machine learning and deep learning models - Build and optimize model architectures including CNNs, RNNs, and transformer-based models - Develop and deploy Large Language Models (LLMs) and object detection systems (e.g., YOLO, Faster R-CNN) - Perform feature engineering and prepare high-quality datasets for training and evaluation - Create and maintain AI/ML training runbooks and documentation - Collaborate with data engineers and software teams to integrate models into production systems - Ensure reproducibility through data versioning and metadata standards - Continuously evaluate and improve model performance and scalability Required Qualifications - Strong proficiency in designing and implementing model architectures, including: - Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - Transformer-based architectures - Large Language Models (LLMs) - Object Detection models (e.g., YOLO, Faster R-CNN) - Hands-on experience with: - PyTorch and/or TensorFlow - Hugging Face, Ollama, or similar frameworks - Experience with data engineering concepts, including: - Feature engineering and dataset preparation - Data versioning tools (e.g., lakeFS) - Metadata standards such as STAC - Ability to create clear and effective AI/ML training runbooks - Strong problem-solving skills and ability to work in a collaborative environment Preferred Qualifications - Experience deploying models in cloud-native environments - Familiarity with DevSecOps practices - Experience working with large-scale or federal datasets - Understanding of MLOps principles and pipelines Benefits & Perks - Weekly pay with full remote flexibility - Professional growth investment, including paid certifications and training - Comprehensive benefits package, including: - Medical, dental, and vision coverage - 401(k) with 100% company match up to 6% - Paid time off (PTO) - Life and disability insurance - Home office equipment plan - A supportive, inclusive team culture focused on collaboration, trust, and mission impact About Rackner Rackner is a cloud-native software consultancy delivering solutions for startups, enterprises, and the public sector. We enable digital transformation through DevSecOps, AI/ML, and cloud-first innovation. Our teams solve high-impact problems that advance federal missions and strengthen national readiness. Join us to help shape the future of secure, scalable data systems supporting mission success.

Detailed Description: - Design, develop, and integrate AI-driven features within industrial control systems and related software platforms. - Implement AI-assisted tooling and automation to improve engineering productivity, testing, diagnostics, or operator workflows. - Collaborate with software developers, hardware engineers, and product stakeholders to identify practical AI use cases aligned with operational needs. - Develop and maintain services that integrate with LLM APIs, local AI models, or intelligent automation frameworks. - Evaluate model performance, reliability, latency, and cost considerations in production environments. - Design safeguards and validation layers to ensure AI-generated outputs meet operational safety and correctness standards. - Contribute to data preparation, structured logging, and telemetry systems that support AI-enabled workflows. - Participate in architectural discussions to ensure AI components integrate cleanly within modular, service-oriented systems. - Utilize AI development tools to accelerate coding, prototyping, documentation, and research while validating all outputs for correctness and maintainability. - Participate in Agile/Scrum ceremonies including sprint planning, backlog refinement, and retrospectives. - Provide accurate estimates, surface technical risks early, and communicate trade-offs clearly. - Support troubleshooting, performance tuning, and continuous improvement of AI-enabled features. - Perform other related duties and special projects, as assigned. Job Requirements: - Demonstrated ability to design and implement production-quality software systems. - Experience integrating external APIs, services, or SDKs into software applications. - Strong understanding of software architecture, modular design, and service-oriented systems. - Working knowledge of data handling, structured formats, and secure API communication. - Ability to evaluate AI-generated output critically for accuracy, bias, safety implications, and operational risk. - Strong verbal and written communication skills, including the ability to explain AI-related technical concepts to cross-functional stakeholders. - Ability to work effectively in a cross-functional, matrixed organization including software, hardware, controls, and operations teams. - Ability to manage multiple initiatives in a fast-paced environment while maintaining quality standards. - Experience using AI-assisted development tools in a professional setting. - Represent the Company and its values with integrity while maintaining high professional and ethical standards in all interactions. Minimum Qualifications: - Bachelor’s Degree in Computer Science, Computer Engineering, Software Engineering, Data Science, or related field (or equivalent practical experience). - 3–6 years of professional software development experience. - Strong proficiency in C#, C++, Python, or comparable programming languages. - Experience working within an Agile development framework. - Experience integrating AI APIs, automation tools, or intelligent systems into applications. Preferred Qualifications: - Experience working with LLM APIs, prompt engineering, embeddings, or vector databases. - Experience building internal AI tooling or workflow automation systems. - Exposure to real-time or hardware-integrated systems. - Familiarity with industrial or Oil & Gas environments. - Experience implementing monitoring, evaluation, or guardrails for AI-enabled systems. - Understanding of latency, throughput, and cost optimization considerations for AI workloads. Additional Details: The Intermediate AI Developer must work with an emphasis on individual safety and safety for others, including coworkers and third parties. Work is primarily performed in a climate-controlled office or remote environment. AI-enabled systems may support software that interfaces with physical equipment; therefore, reliability and operational awareness are essential. The employee is regularly required to sit, stand, or use computer equipment for extended periods. Salary Range: $111,800 - $132,500 CAD

Role Description We’re hiring an Explainable AI Engineer to build, integrate, and deploy our AI Engines and Agentic AI workflows. You’ll have an opportunity to learn from Stanford and Georgia Tech professors and work across algorithms and data pipelines to evolve our Explainable AI including: - Development of deep math features for the models, ML, and inferences - Automation of data wrangling and cleansing towards scalability - Aerospace grade V&V for mission critical functions using simulations - Explainable AI engine interfaces to data, human users, and Agentic AI Main Responsibilities: - Understand, develop, & support deep math analytics in Explainable AI Engines - Build, evolve, verify, and validate data processing pipeline segments - Support scalable deployment of the AI - Integrate Explainable AI with LLM tools for Agentic AI experience - Collaborate with the cloud software team and occasionally with customers - Document Explainable AI for customer users and internal software developers Qualifications - Experience with productized analytical applications - Experience building mission-critical applications as a part of a team - Hands-on skills in at least some of mathematical methods in: - Statistics - Bayesian estimation - Mathematical optimization - Decision and Control - Experience with MATLAB - Experience in Java and GitHub (Actions) - Solid experience writing technical documentation - Strong collaboration and communication skills - Bachelor’s Degree in Engineering or a related field - U.S. Citizenship Requirements - Model-based development / simulation-based verification - Operations Research - Supply chain and/or MRO in the aerospace industry - Building SaaS type applications - Shell scripting, Golang - Experience with GenAI and LLM augmentation (RAG) - An advanced degree Additional Information - US Citizenship Required: This position requires the ability to work with Controlled Unclassified Information for the Department of Defense. - A background check and a drug check may be required. - Location: This is a remote position. USA, Nationwide. Proximity to either our Palo Alto, CA or Atlanta, GA office may be a plus. - You will have an opportunity to start as a contractor (preferred; full-time or part-time, at least 10 hours/week), or a full-time employee.