Simplesense builds, deploys, and sustains the Installation Resilience Platform that enables mission operators to rapidly adapt and respond. The Platform protects critical infrastructure from cyber attack while unlocking previously siloed information to monitor, diagnose, and improve response times to incidents. Our adversaries rapidly adopt the latest technology: we help defense users respond in kind.

Simplesense is a non-traditional defense contractor and prime on the Air Force's Installation Resilience Operations Command and Control (IROC) program, which is now expanding to five additional Air Force, Space Force, and Army installations from the one prototype installation, Tyndall Air Force Base.

Our team combines over 100 years of direct mission experience solving hard problems with 50 years technical expertise deploying DevSecOps, cybersecurity, and cloud infrastructure, giving us a deep appreciation for our customers' mission and end users' priorities. We build for scale, architecting and prioritizing technical work for long term sustainability.

Simplesense's engineering team drives innovation by developing and integrating advanced technologies to deliver real-time solutions that enhance security, operational efficiency, and data fusion into mission critical systems. We design and implement secure infrastructure systems, including operational technology (OT) gateways, cloud-based analytics, and automated monitoring, to streamline data flow and enable actionable insights. By focusing on resilience, scalability, and continuous improvement, our engineering team creates robust platforms that empower decision-making and mission success. Collaboration across public and private systems ensures our solutions bridge critical gaps, optimize resource use, and meet the demands of our customers.

Simplesense is looking for a Research & Development Software Engineer to join our remote, US-based team. In this role, you will help design and deliver data-driven capabilities within our Installation Resilience Platform, with a focus on applied AI and large-scale operational data. You'll build cloud-native components that support machine learning workflows, integrate with sensor and cybersecurity systems, and make complex infrastructure data accessible through natural language interfaces. This position combines production-grade software development with real-world mission relevance. Occasional travel for on-site collaboration or operational support may be required.

We're looking for candidates who thrive in cloud-native, remote-first environments and who bring a mix of software engineering experience and interest in AI-driven capabilities. Our stack includes Amazon Web Services, containerized workloads, and data from operational environments demanding reliability, security, and clarity. You don't need to meet every listed qualification as we value curiosity, learning, and the ability to contribute meaningfully to a small, high-trust team working on real-world challenges.

Qualifications

5+ years of experience in software or data engineering deploying to production cloud or on-premise environments.
Hands-on experience with Python, or similar, for data processing, pipeline development, or machine learning workflows.
Experience with retrieval augmented generation (RAG), embeddings, or GenAI pipelines.
Proficiency in AWS services commonly used in data/AI systems such as Lambda, S3, RDS, SageMaker, Bedrock, DynamoDB, or Athena.
Experience developing and operating systems in Linux environments.
Comfort building and debugging containerized applications using Docker, ECS, Kubernetes, or similar.
Experience working with GitHub based workflows, CI/CD automation, and branching strategies.
Bachelor's degree in Computer Science, Data Science, Software Engineering, or related field.
Must be a U.S. Citizen and able to obtain a DoD NIPR account and Common Access Card (CAC).

Bonus Qualifications

Experience working with real-world telemetry, log, or sensor data
Familiarity with secure architecture, FedRAMP environments, and/or RMF aligned systems.
Security certifications such as Security+ or AWS Certified Solutions Architect

Core Responsibilities

AI Workflow Integration: Support the development of ML/LLM-enabled capabilities, including retrieval workflows, inference services, and fine-tuning pipelines using frameworks such as SageMaker.
Cloud Native Application Engineering: Develop and deploy backend services and cloud functions, such as Lambda, ECS, or DynamoDB, to support mission workflows and data-driven capabilities.
Automation and DevOps: Contribute to build/test/deploy pipelines and maintain scripting for automated development environments and deployments
Observability and Reliability: Implement monitoring and alerting for AI/ML and data services to ensure availability and performance across environments
Collaboration: Partner with infrastructure, UI, and mission-facing teams to shape solutions and ensure contextual relevance of features.
Research and Prototyping: Contribute to rapid prototyping and research efforts across government and dual-use applications, with opportunities to shape applied GenAI use cases.
Code Quality and Documentation: Write maintainable, well-documented code and contribute to peer reviews, technical write-ups, and internal learning.

Competitive Benefits