R&D Software Engineer — AI/ML Mission Solutions

R&D Software Engineer — AI/ML Mission Solutions

Location: United States
Work Model: Remote
Travel: Approximately 15% for R&D events, technical demos, and collaboration sessions
Clearance: U.S. citizenship required. Active Secret clearance preferred; candidates must be eligible to obtain and maintain a U.S. government security clearance.

Build AI/Data Capabilities for Mission-Focused R&D

Rackner is hiring an R&D Software Engineer — AI/ML Mission Solutions to help build, prototype, validate, and improve AI/data-driven software capabilities for defense-relevant use cases.

This is a hands-on R&D role for an engineer who enjoys solving hard problems across software, data, AI/ML, algorithms, RAG, agentic systems, simulation, or pipeline workflows. You will work with Rackner’s internal R&D team to turn ambiguous ideas, operational needs, and user feedback into working technical capabilities.

We are not looking for one person to be an expert in every area. The strongest candidates bring one deep technical lane — such as AI/ML systems, RAG/agentic workflows, data pipelines, Python/Go development, Kubernetes/deployable systems, MLOps, or algorithmic software — plus enough working knowledge in adjacent areas to contribute in a fast-moving R&D environment.

This is not a traditional sales role and not a platform-only engineering role. Kubernetes, Terraform, cloud, CI/CD, and DevSecOps are helpful, but the main focus is software engineering, AI/data systems, algorithmic problem-solving, validation, and R&D execution.

What You'll Do

• Design, prototype, test, and refine AI/data-driven software capabilities for mission-focused use cases
• Build software for AI/ML experimentation, RAG or agentic workflows, model integration, simulation, data pipelines, and applied R&D prototypes
• Develop and improve data workflows, schema transformations, JSON workflows, APIs, backend services, and integration layers
• Support AI/ML workflows including inference, evaluation, deployment, monitoring, and model-serving patterns
• Validate outputs through testing, data quality checks, evaluation methods, debugging, monitoring, and failure analysis
• Troubleshoot complex issues across software, data, model, and integration layers
• Work with engineers and technical leadership to assess tradeoffs, identify constraints, and improve solution design
• Participate in technical demos, R&D events, mission-user discussions, and feedback cycles as needed
• Convert stakeholder or mission feedback into actionable technical steps
• Clearly explain technical concepts to engineering teams, program stakeholders, customers, and mission users

What You’ll Bring

• Strong software engineering background with hands-on experience building, testing, debugging, and improving modern systems
• Proficiency in Python, Go, C++, Java, SQL, or a similar language used for backend, data, AI/ML, or algorithmic work
• Hands-on depth in at least one relevant area: AI/ML systems, RAG, agentic development, LangChain, data pipelines, MLOps, algorithmic software, simulation, or deployable systems
• Ability to clearly explain what you personally built, how the system worked, what broke, how you validated it, and what impact it had
• Familiarity with real-world data workflows, including data sources, APIs, schemas, transformations, databases, files, events, or logs
• Comfort working in fast-paced environments with evolving requirements
• Willingness to travel approximately 15% for R&D events, demos, collaboration sessions, or customer and mission engagements

Experience That Stands Out

• DoD, Air Force, Platform One, Big Bang, mission planning, C2, ISR, autonomy, or defense technology experience
• AI/ML systems, applied AI workflows, RAG, agentic development, LangChain, model integration, evaluation, deployment, serving, registry, or monitoring
• MLOps tools or workflows such as MLflow, SageMaker, Databricks, Kubeflow, Airflow, Dagster, Prefect, or similar tools
• Data pipelines, ETL/ELT workflows, schema transformations, JSON transformations, dbt pipelines, or messy data integration workflows
• Data validation, data quality checks, pipeline monitoring, failure handling, debugging, or observability for data/model workflows
• Algorithms, optimization, simulation, scientific computing, applied mathematics, physics-informed software, computer vision, autonomy, or robotics
• Python, Go, C++, SQL, PyTorch, TensorFlow, scikit-learn, FastAPI, Postgres, or related software/data/AI tooling
• Technical demos, pilots, field exercises, workshops, briefings, or customer / mission-user discussions
• Cloud, Docker, Kubernetes, Terraform, CI/CD, DevSecOps, ATO, or secure delivery experience

About Rackner

Rackner is a software consultancy focused on building mission-critical systems for the U.S. government. Our teams work across cloud platforms, DevSecOps, AI/ML, distributed systems, and modern software engineering initiatives supporting federal agencies and national security missions.

Rackner engineers and technical teams collaborate closely with leadership, program teams, and mission stakeholders to design, demonstrate, and improve software systems that address complex operational challenges.

Benefits & Perks

Rackner invests in its people, because when you grow, we all win.

• Company-supported certifications aligned to current and future program work, including cloud, Kubernetes, DevSecOps, security, AI/ML, project management, and related technical areas
• Clear advancement tracks and future leadership opportunities
• 401(k) with 100% match up to 6%
• Comprehensive medical, dental, vision, life, and disability coverage
• Generous PTO and paid holidays
• Home-office equipment plan and remote work support
• Fitness and wellness reimbursement
• Weekly pay schedule and modern perks, including team events

Equal Opportunity

Rackner is an equal opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or other protected characteristics.