Software Engineer, Data Infrastructure

Scale AI is seeking a highly skilled and motivated Mission Software Engineer to join our dynamic Federal Engineering team. As a part of this team, you will play a critical role in supporting Scale’s government customers by scoping and developing onsite solutions. Our scalable, high-performance platform is the foundation for these customer solutions, and your expertise will be instrumental in designing and implementing systems that can handle interactions with existing customer systems to help our products integrate into existing customer workflows.   

The Role

• We are looking for an exceptional Senior Software Engineer to architect and build the foundational data infrastructure that will serve as the brain of a project ecosystem.
• We are not looking for someone to stitch together off-the-shelf data frameworks. You will be responsible for designing highly novel data models and processing pipelines capable of handling massive quantities of output data from complex simulations.
• At the core of this role is the challenge of building a foundational data ensemble—a unified architecture that seamlessly aggregates, structures, and stages diverse sources of simulation outputs and user inputs. Your systems will manage enormous batch throughput jobs with strict, minimal latency requirements, ensuring that downstream AI systems and language models have the exact context they need to actionably reason over complex, multi-dimensional scenarios.

Key Responsibilities

• Architect the Data Ensemble: Design and implement the architecture to ensemble various sources of injected context (deeply structural simulation data, historical game states, and dynamic user inputs) into a unified, highly queryable format optimized for LLM consumption.
• Massive Batch Infrastructure: Build highly scalable, resilient data architectures from scratch. You will optimize for moving, transforming, and processing massive quantities of simulation output data via enormous batch jobs, maintaining the minimal latency required for rapid wargame iterations.
• Complex Data Modeling: Design sophisticated, highly relational data models that accurately represent massive, state-based simulation environments, making them easily interpretable by machine learning models.
• First-Principles Problem Solving: Navigate highly ambiguous product requirements to design custom, ground-up systems where existing open-source or enterprise tools simply cannot handle the structural complexity or scale.
• Technical Leadership: Set the technical standard for the data infrastructure team, driving rigorous code quality, system performance, and architectural clarity.

What We’re Looking For

• Experience: 5+ years of backend or data infrastructure experience, operating at a Senior, Staff, or Principal level.
• Engineering Excellence: Deep, expert-level proficiency in systems languages (e.g., Rust, Go, C++, or highly optimized Python/Java, Spark) and a fundamental understanding of memory management, compute limits, and distributed systems architecture.
• High-Throughput / Low-Latency Data: Proven track record of processing massive datasets. You understand how to optimize massive batch jobs and parallel processing across distributed simulation nodes without sacrificing speed.
• Information Retrieval & Context Surfacing: You don't need a background in AI agents, but you must be an expert in surfacing the right needle from an ocean of hay to feed decision-making engines. We highly value engineers with backgrounds in:
• Search & RecSys: Building complex information retrieval systems or recommendation engines.
• Gaming / MMOs: Managing complex state, data relationships, and telemetry for massive, highly populated simulations.
• High-Frequency Trading (HFT): Processing disparate, massive streams of data for algorithmic decision-making.
• Mission-Driven: A strong desire to build robust, foundational technology that supports national security and defense modernization.

Nice to Have

• • Security Clearance: An active Secret or TS/SCI clearance is a nice to have for this role. If you do not have an active clearance, you must be eligible and willing to obtain one.
  • Experience with LLM context optimization, vector embeddings, or agentic AI frameworks (e.g., advanced RAG architectures).
  • Deep domain experience working with wargaming data, complex systems modeling, or distributed simulation protocols.
  • Previous experience in a high-growth, 0-to-1 startup environment.

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. 

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision. 

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