Interested in this AI/ML Engineer role at Calimontex Technologies LLC?
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About This Role
Calimontex Technologies LLC is looking for a motivated and aspiring AI/ML Engineer to join our team in a remote, full-time capacity. This opportunity is ideal for recent master's graduates from U.S. universities who want to build their careers in Artificial Intelligence, Machine Learning, and Generative AI.
We welcome early career professionals with strong academic foundations, hands on experience through internships or university projects, and a passion for solving real world problems using AI/ML technologies.
We also support eligible candidates navigating the STEM OPT extension process, subject to applicable requirements.
Key Responsibilities
- Develop, train, test, and evaluate machine learning models for real world applications.
- Write clean, efficient, and maintainable Python code for AI/ML solutions.
- Work with structured and unstructured datasets to perform data preprocessing, feature engineering, and exploratory data analysis.
- Apply machine learning algorithms for classification, regression, clustering, and predictive analytics.
- Explore and implement deep learning, Natural Language Processing (NLP), and Generative AI solutions as applicable to project requirements.
- Use libraries and frameworks such as Scikit-learn, TensorFlow, PyTorch, Pandas, and NumPy.
- Assist in building and integrating AI models into applications, APIs, and data pipelines.
- Evaluate model performance, troubleshoot issues, and improve accuracy and efficiency.
- Collaborate with team members to understand project requirements, document technical findings, and deliver solutions.
- Stay updated on emerging AI/ML technologies and contribute to continuous learning and innovation.
Required Qualifications
- Master's degree completed from an accredited university in the United States in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, or a related technical field.
- 0–2 years of professional experience in AI, Machine Learning, Data Science, or a related area.
- Strong foundation in Python programming, data structures, algorithms, and fundamental machine learning concepts.
- Familiarity with Pandas, NumPy, Scikit-learn, or similar data science libraries.
- Understanding of supervised and unsupervised learning, model evaluation, and data preprocessing.
- Experience with academic projects, capstone projects, research, internships, or personal AI/ML projects.
- Strong analytical, problem-solving, communication, and teamwork skills.
- Current U.S. work authorization appropriate for the position.
Preferred Qualifications
- Exposure to TensorFlow, PyTorch, Keras, or other deep learning frameworks.
- Familiarity with Generative AI, Large Language Models (LLMs), prompt engineering, or Retrieval-Augmented Generation (RAG).
- Basic understanding of REST APIs, Git/GitHub, SQL, and software development practices.
- Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- A portfolio of academic projects, GitHub repositories, research work, or practical AI/ML implementations.
Benefits and Career Development
- STEM OPT Support: Assistance with the STEM OPT extension process for eligible candidates, subject to program requirements.
- Remote Work: Opportunity to work remotely within the United States, subject to applicable role and training requirements.
- Practical Experience: Opportunity to apply academic knowledge to real-world AI/ML projects.
- Technical Mentorship: Guidance and professional development opportunities for early career engineers.
- Career Growth: Opportunities to strengthen AI/ML engineering, programming, and problem-solving skills.
- Collaborative Environment: Work with a team focused on technical learning, innovation, and continuous improvement.
Who Should Apply?
This position is particularly suitable for:
- Recent master's graduates from U.S. universities.
- Candidates currently working under F-1 OPT who are interested in pursuing a STEM OPT extension, if eligible.
- Early-career professionals with 0–2 years of experience.
- Candidates with strong academic, internship, research, or capstone project experience in AI/ML.
- Aspiring AI/ML engineers seeking hands-on industry experience and professional growth.
How to Apply
Interested candidates are encouraged to submit their updated resume highlighting their education, technical skills, AI/ML projects, internships, and relevant experience.
Join Calimontex Technologies LLC and take the next step in your AI/ML engineering career!
Pay: $70.00 - $100.00 per hour
Expected hours: 30.0 per week
Benefits:
- Employee assistance program
- Flexible schedule
- Paid time off
- Professional development assistance
Application Question(s):
- When you have finished your highest education ?
Education:
- Master's (Preferred)
Work Location: Remote
Salary Context
This $145K-$208K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Calimontex Technologies LLC, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($176K) sits 17% below the category median. Disclosed range: $145K to $208K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Calimontex Technologies LLC AI Hiring
Calimontex Technologies LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $208K - $208K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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