Staff ML Engineer – Agentic AI for Geospatial Systems

Fdetimes; Denver (CO); On-site; USD 274,000 - 343,000; Full time

Benefits offered by this job

Health, dental, vision coverage

Equity or equity-based compensation

Learning and development stipend

Generous PTO

Job summary

Scale is seeking a Staff Machine Learning Engineer to lead the design and deployment of agentic AI systems for government-grade, mission-critical environments. You will shape architectures, own core libraries, and mentor teams while partnering with defense and intelligence programs to deliver robust, scalable AI capabilities.

You will drive long-horizon reasoning systems, retrieval embeddings, and memory architectures, ensuring production-grade reliability and performance across complex data and

Qualifications

• 8+ years of experience building and deploying applied ML systems in production environments.
• Deep experience with agentic systems, autonomous workflows, or ML systems that reason and act over multiple steps.
• Strong background in ML systems engineering, including model serving, pipelines, monitoring, and evaluation.
• Hands-on experience with retrieval systems, embeddings, or representation learning.
• Proficiency in Python and modern ML frameworks (ex: PyTorch), with the ability to design systems end to end.
• Demonstrated ability to operate at Staff-level scope: setting technical direction, owning ambiguous problems, and driving 0→1 initiatives to production.
• Experience making thoughtful tradeoffs across performance, cost, reliability, and development velocity.

Responsibilities

• Lead the architecture and implementation of agentic AI systems, with a focus on long-horizon reasoning, orchestration, and system-level reliability.
• Build and scale agents that perform complex geospatial reasoning, including interpreting, generating, and reasoning over maps and spatial data.
• Design and improve retrieval systems across large collections of static and semi-structured documents, enabling agents to surface high-signal context efficiently.
• Fine-tune and evaluate embedding models to improve recall and precision for mission-critical datasets.
• Design memory systems that allow agents to persist state, operate over long contexts, and learn from prior interactions.
• Own and evolve shared agentic infrastructure and core libraries, enabling reuse across teams, products, and Public Sector contracts.
• Define evaluation strategies for agentic systems, including robustness testing, failure-mode analysis, and regression testing in production environments.
• Partner closely with engineering managers, product leaders, and researchers to scope high-impact initiatives and unblock execution across teams.
• Serve as a technical mentor and multiplier—raising the bar for system design, ML rigor, and production readiness across the organization.
• Comfortable with light travel (approximately 10%) for customer interaction and team needs.

Skills

Agentic ML systems

Geospatial reasoning

Retrieval systems

Embedding models

Python

PyTorch

Model serving

Systems engineering

Production deployment

Staff-level leadership

Tools

ML frameworks

Pipelines

Monitoring

Evaluation

Job description

Scale is seeking a Staff Machine Learning Engineer to lead the design and deployment of agentic AI systems for government-grade, mission-critical environments. You will shape architectures, own core libraries, and mentor teams while partnering with defense and intelligence programs to deliver robust, scalable AI capabilities.

You will drive long-horizon reasoning systems, retrieval embeddings, and memory architectures, ensuring production-grade reliability and performance across complex data and