AI/ML Engineer II

Torch.AI•Leawood, KS

22d•Onsite

About The Position

Torch.AI builds a government-owned reasoning infrastructure which creates a Reasoning Layer to enable machine reasoning at scale, in environments where it is hardest to achieve and least tolerant of failure. This is not incremental software. It is long-term infrastructure designed to endure, integrate, and evolve alongside the systems it supports. The Reasoning Layer is a modular architecture where data is connected, governed, transformed, represented, fused, reasoned over, and delivered into mission applications and operational workflows. It does not replace systems of record. It enables them to function better together. The Reasoning Layer is comprised of: ORCUS (ingestion, orchestration, governance), NEXUS (semantic representation, vectorization), HALO (graph-based fusion and reasoning) and various product and capability components deployed for specific customer use cases. We are seeking candidates who approach problems creatively, are comfortable operating without full clarity, and take responsibility for outcomes, not just implementation. If you’re driven to strengthen U.S. defense readiness and protect national interests, Torch.AI offers meaningful impact at national scale. Torch.AI was founded on a simple operational insight: better use of data leads to better decisions. As data volumes increased across commercial, enterprise, and national security environments, the limiting factor was not collection, storage, or user interface design. The missing layer was machine understanding. And an infrastructure that could operate and reason between layers, preserve context, reconcile meaning, and make data useful for decisions in real time. We believe the government must own its data and decision environment. In mission and operational environments, the layer where data becomes context, context informs models, models support decisions, and decisions shape action cannot be controlled entirely by private technology vendors. Ownership does not mean the government must build every component itself. It means the government maintains stewardship and authority over the mission and operational layer. Commercial software, models, and services can contribute to the environment, but they should not capture the mission. We are craftsmen. We build with intention. We ship with discipline. You’ll collaborate with engineers, data experts, veterans, and mission practitioners. You’ll own meaningful work, move quickly, and see your systems deployed in production, often within weeks. We are fast-paced, entrepreneurial, and mission-driven. Every day is a new puzzle.

Requirements

B.S. or M.S. in Computer Science, Engineering, or related technical field.
3–6 years of experience building applied ML systems or NLP workflows.
Strong Python development skills with ability to write production-quality services.
Experience training, tuning, evaluating, and deploying ML models.
Familiarity with modern ML/NLP libraries (Transformers, spaCy, scikit-learn, PyTorch).
Exposure to cloud environments and containerized deployment patterns.
Strong communication skills and ability to collaborate across teams.
U.S. citizenship is required for all positions.
Eligibility to obtain and maintain an active Secret, Top Secret, or Top Secret/SCI clearance may be required.

Nice To Haves

Experience with embeddings, vector search, RAG, and semantic retrieval systems.
Familiarity with MLflow, DVC, Kubeflow, SageMaker, or similar tooling.
Experience with graph-based retrieval, agentic systems, or tool-use architectures.
Experience supporting defense, intelligence, ISR, or mission environments.

Responsibilities

Design and implement end-to-end ML workflows supporting semantic search, classification, entity resolution, and retrieval.
Build production-ready AI services with strong attention to reliability, testability, and maintainability.
Develop and tune embedding pipelines, retrieval systems, and retrieval-augmented generation (RAG) components.
Collaborate with data engineering and backend teams to integrate ML capabilities into scalable systems.
Implement evaluation workflows tied to measurable mission performance (accuracy, latency, robustness).
Support deployment, monitoring, and versioning of ML models as part of a disciplined MLOps lifecycle.
Participate in architecture discussions and propose solutions aligned to platform constraints and mission needs.