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About This Role
Torch Technologies
Thank you for your interest in employment with Torch Technologies. We are a 100% employee\-owned, Certified Great Place To Work and named Best Places to Work in Huntsville/Madison County, headquartered in Huntsville, AL. Our team provides superior research, development, and engineering services to the Federal Government and Department of War. As one of the nation’s top 100 defense companies, the services we provide directly support the men and women who serve our country. Our corporate mission sums up the pride our employee\-owners take in the work we do: “Lighting the Pathway of Freedom”. And, as a Certified Evergreen ESOP, we have made the commitment to grow and sustain our company for the next 100 years! Come grow with us!
Torch Technologies is seeking a highly analytical and mathematically minded Artificial Intelligence/Machine Learning (AI/ML) Engineer to join our AI/ML team. The candidate will support the Missile Defense Agency’s (MDA) Ground Test scenario design team. Our team is comprised of scenario design engineers, software developers, sys admins, and the AI/ML team. We work together to produce test scenarios that provide data for decisions on the MDS development and readiness. Our AI/ML team develops models and tools that enhance our design process, enabling us to “Go Fast, Think Big”.
The team is committed to developing solutions that are grounded in a deep understanding of the underlying algorithms, data, and systems. We prioritize explainability, reliability, and rigor in our work, and are seeking an AI/ML Engineer who shares these values. If you're passionate about applying AI/ML to complex problems and driving technical innovation, this role offers a unique opportunity to make a meaningful impact.
As an AI/ML Engineer, your duties will include the following, but are not limited to:
- Design, develop, and deploy machine learning models to support mission\-critical objectives.
- Analyze and optimize model performance, identify areas for improvement, and implement data\-driven solutions.
- Collaborate with cross\-functional teams to integrate machine learning solutions into existing systems and workflows.
- Develop and maintain scalable, high\-quality code using Python and related technologies.
- Research and apply emerging AI/ML technologies, frameworks, and methodologies to drive innovation.
- Critically evaluate machine learning systems, including model assumptions, explainability, and potential biases.
- Contribute to best practices, technical standards, and methodologies for AI/ML development and deployment.
- Support technical documentation, reporting, and presentation of findings to internal and external stakeholders.
Required Qualifications
- U.S. Citizenship.
- Bachelor’s Degree in Physics
- 2\+ years of relevant experience developing AI/ML models and machine learning solutions.
- Ability to obtain and maintain DoW security clearance
- Experience with machine learning frameworks such as PyTorch and advanced modeling strategies.
- Strong analytical, critical thinking, and problem\-solving skills.
- Ability to work independently and collaboratively in a team environment.
- Excellent written and verbal communication skills with the ability to explain complex technical concepts clearly.
- Experience supporting defense, engineering, scientific computing, or technical mission environments.
Preferred Qualifications
- Experience supporting DoD or Federal programs.
- Familiarity with structured data analysis, simulations, or scientific computing environments.
- Experience applying AI/ML solutions to complex real\-world problems.
- Interest or experience in explainable AI, interpretable modeling, or advanced AI techniques.
- Experience working with cross\-functional engineering and technical teams.
- Strong technical documentation, briefing, and reporting experience.
- Strong self\-directed learning skills with the ability to independently research and synthesize advanced technical concepts.
- Experience with state\-of\-the\-art agentic coding architectures and tools.
Schedule : M\-F; 8\-5
Work Location : Huntsville, AL
Travel : Yes, 0%\-10%
Relocation Assistance Available : No
Position Contingent Upon Award of Contract : No
\#LI\-DK1
Benefits:
Torch Technologies is proud to offer a stable and professional work environment, a competitive salary, and an excellent, comprehensive benefit package including: ESOP participation, 401(k) match, medical, dental, vision, life insurance, short\-term disability, long\-term disability, flexible spending accounts, Health Saving Accounts and Health Reimbursement Accounts, EAP, education assistance, paid time off, and holidays.
Applying to Torch Technologies:
Only those candidates invited for an interview will be contacted. Employment at Torch Technologies is contingent upon the successful completion of a comprehensive background check.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, citizenship, ancestry, marital status, protected veteran status, disability status or any other status protected by federal, state, or local law. Torch Technologies, Inc. participates in E\-Verify.
If you are a qualified individual with a disability or a disabled veteran, you have the right to request a reasonable accommodation if you are unable or limited in your ability to use or access Careers Link as a result of your disability. You can request reasonable accommodations by sending an email to [email protected]. Thank you for your interest in Torch Technologies.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Torch Technologies, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Torch Technologies AI Hiring
Torch Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Redstone Arsenal, AL, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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