AI/ML Engineer I

The world’s most consequential systems need the world’s best builders.

A new era is taking shape. AI is no longer confined to models or interfaces. It is becoming the foundation of how decisions are made across governments, industries, and real-world environments. What matters now is not just access to capability, but how it is applied, controlled, and sustained over time.

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.

Not another dashboard. Not another workflow app. Not another black-box model glued to a PowerPoint.

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.

What Makes Torch.AI Different

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.

The Type of Candidates Who Thrive

People who do well here tend to approach problems similarly.

• They are comfortable operating without full clarity.
• They pay attention to what actually happens, not just what was intended.
• They are willing to be wrong, adjust quickly, and improve based on real feedback.
• They take responsibility for outcomes, not just implementation.

This is not a good fit for someone who needs tightly defined problems or prefers distance from how their work is used.

Security Clearance

Some roles require an active Secret, Top Secret, or Top Secret/SCI clearance. Where required, candidates must be eligible to obtain and maintain the appropriate clearance level.

U.S. citizenship is required for all positions. Torch.AI does not sponsor employment visas.

If you do not currently hold a clearance but are eligible, sponsorship may be available depending on role and mission requirements.

Work Location

Most roles are based at our headquarters in Leawood, KS. Some hybrid/remote positions across the Arlington, VA, Washington, DC, and Maryland (DMV) region are available. Limited travel (<10%) may be required for some roles.

Compensation, Benefits, Incentives

We offer competitive, performance-aligned compensation tied to technical depth, clearance level, and mission impact.

Total Rewards Include:

• Competitive base salary
• Quarterly performance bonuses
• Equity participation within the first 12 months
• Unlimited PTO + 11 paid company holidays
• Professional development in a high-growth, mission-driven environment
• Weekly in-office catering at HQ

Benefits:

• 401(k) plan (no current employer match, but under consideration)
• PPO, HSA, and TRICARE Supplement medical options
• Above-market HSA contributions
• HSA, FSA, and Dependent Care FSA options
• Dental and vision plans above national averages
• Employer-paid life insurance (1× salary)
• Employer-paid Short-Term and Long-Term Disability
• Voluntary Accident, Critical Illness, and Hospital Indemnity coverage
• Up to $300/month in tax-advantaged commuter benefits

Torch.AI is an Equal Opportunity / Affirmative Action Employer committed to building a team that reflects the mission we serve.

If you want to build AI systems that move from ingestion to actionable insight and know exactly why they produced the answer they did, we should talk.

What You’ll Do

• Implement and evaluate machine learning components under senior guidance, focusing on operational performance rather than academic novelty.
• Support data preparation, labeling, preprocessing, and feature engineering across structured and unstructured datasets.
• Contribute to retrieval, embedding, or LLM-enabled pipelines with mentorship.
• Assist in building prototype and production-ready AI services using frameworks such as PyTorch, scikit-learn, or modern NLP toolkits.
• Execute experiments for NLP/NLU workflows including classification, entity extraction, semantic search, and retrieval.
• Support model evaluation using measurable metrics aligned with latency, accuracy, and mission constraints.
• Learn foundational deployment patterns including containerization, API exposure, monitoring, and model lifecycle practices.
• Participate in code reviews, sprint planning, documentation, and disciplined software development practices.

Core Skills & Qualifications

• B.S. in Computer Science, Engineering, Mathematics, or related technical field.
• 1–3 years of experience in software engineering or applied ML (internships included).
• Proficiency in python 
• Strong Python fundamentals and ability to write clean, testable code.
• Familiarity with at least one ML/NLP library (e.g., scikit-learn, spaCy, PyTorch, TensorFlow, Transformers).
• Foundational understanding of ML workflows: data preprocessing, training loops, validation, and evaluation metrics.
• Basic understanding of APIs, containerization, or cloud environments is preferred but not required.
• Ability to work in ambiguous problem spaces and iterate quickly with guidance.
• Strong communication skills and willingness to collaborate across engineering disciplines.

Additional Valuable Experience

• Exposure to LLMs, embeddings, semantic search, or retrieval pipelines.
• Familiarity with vector databases and/or graph-based data systems.
• Experience working with constrained environments (edge devices, low-SWaP hardware).
• Understanding of real-time or latency-sensitive ML systems.
• Exposure to defense, ISR, or mission-oriented problem domains.
• Experience integrating ML outputs into operational workflows.