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About This Role
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
- 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.
Core Skills \& Qualifications
- 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.
Additional Valuable Experience
- 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At TORCH.AI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
TORCH.AI AI Hiring
TORCH.AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Leawood, KS, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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