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About This Role
Company Overview:
Macalogic is a company with broad competencies based on over a decade of experience working in the federal arena. We are a Woman\-Owned SBA\-certified Small Business and an Economically Disadvantaged Woman\-Owned Small Business (EDWOSB). We provide business consulting services to government clients in five primary areas: software development and IT\-related consulting; cybersecurity, program support; federal enterprise architecture; and compliance. In the commercial, Business\-to\-Business (B2B) arena, we provide management support services, including hardware, software, and network installation, troubleshooting, and management.
Our core values of “Building Trust”, “Showing Up”, “Owning It”, “Getting Better”, and “Serving Others” are central to everything we do at Macalogic. We offer a generous benefits package and compensation commensurate with your experience and skill set.
General Description
This position provides senior artificial intelligence software development support to the Department of Commerce (DOC), International Trade Administration (ITA). The successful candidate will develop, operate, repair, and maintain systems, applications, and IT environments in support of AI applications and/or system components. This includes supporting/performing requirements analysis, planning, design, deployment, and ongoing operations management and technical support, all while employing SAFe agile practices.
Duties and responsibilities
The successful candidate will be responsible for developing algorithms and building predictive models from the ground up, using machine learning and deep learning frameworks. This includes but is not limited to scaling prototypes into Machine Learning (ML) models, preprocessing data, automating and orchestrating ML pipelines, managing new and existing data and models, ensuring the quality and accuracy of data fed into models, serving and scaling models, and deploying and monitoring the ML solutions. Other specifics include:* Designing and developing AI models, algorithms, and applications to solve specific problems
- Collaborating to preprocess and analyze large datasets
- Implementing machine learning and deep learning algorithms and frameworks
- Fine\-tuning AI models for improved performance and accuracy
- Documenting code, algorithms, and processes for future references
- Integrating AI models into production systems
Specialized Knowledge
The successful candidate will have knowledge in the following areas:* Knowledge and expertise with languages like Python.
- Create software code and automated test and build scripts to ensure adherence to established configuration and change management principles.
- Experience with developing and executing test plans, test scripts, and test tools to minimize defects
- Knowledge of how to diagnose problems, think critically, organize information, and develop optimal solutions for complex issues.
Education
Bachelor's degree in information technology, computer science, or related fields of study.
Professional Certifications
None required, but Microsoft Certified: Azure AI Engineer Associate or similar certification is preferred.
Clearance Required
Must have completed or be able to complete a Tier 1 National Agency Check and be successfully adjudicated to occupy a Public Trust position.
Experience
Minimum of 2 years’ experience developing and maintaining AI solutions.
U.S. Citizen or Similar
Must be a U.S. Citizen
Other Characteristics (e.g., Personal or Language)
Excellent oral and written communication skills in English. Must be able to work in a team environment with members that include personnel from other companies, Government program management and administrative personnel, and technical staff members. Must be able to perform with minimal management oversight and have strong time management skills.
Working conditions, including location
Remote work environment.
Client duty hours are Monday through Friday.
Salary
$110 to $ 120k annually
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Salary Context
This $110K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Macalogic, 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. This role's midpoint ($115K) sits 47% below the category median. Disclosed range: $110K to $120K.
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.
Macalogic AI Hiring
Macalogic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $120K - $120K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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