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About This Role
Team CATHEXIS elevates the government contracting experience through rapid response, deep skill, and thoughtful problem\-solving and communication. Our core capabilities are our top\-tier program and project management, data analytics, and audit services, the backbone of which is our integrated approach to operational excellence.
You worked hard to get to where you are. You strive to make every day better than the day before. So do we. Team CATHEXIS operates with an all\-in mindset. We are working together to create a company that supports our shared values and individual goals. Our values are centered around leading with integrity, owning the outcome, growing together, and moving with purpose in everything we do for our employees, customers, partners, and communities. We believe success is best when we listen and lead with empathy; model high standards of ethics to provide a rewarding candidate experience; work hard, have fun, and appreciate the strengths we all bring to the team; and empower our employees to create innovative and trusted results.
We are looking for a dynamic AI/ML Software Developer to join our team. The AI/ML Software Developer will work alongside a team of highly skilled engineers in the development of AI applications. A motivated and qualified candidate will not only have hands\-on development experience in (JavaScript, Python or Java) but also a willingness to collaborate with teams to solve problems. We’re seeking curious and creative technical staff who are excited about delivering quality solutions.
ResponsibilitiesThe responsibilities include, but are not limited to:
- Collaborate with other developers, staff and leadership to design solutions that solve customer problems
- Design, develop, and maintain technical solutions
- Build and improve tools for users to understand and analyze large\-scale data
- Perform debugging, troubleshooting, modifications and testing of software and solutions
- Develop documentation, and collaborate in the development of technical procedures and user support guides
Requirements* This position requires the ability to obtain and maintain a Secret security clearance. U.S. citizenship is required, as it is a condition of obtaining the clearance necessary for this role. Selected candidates must be able to successfully complete a U.S. Government background investigation.
- Bachelor’s degree in computer science or related field
- 3\+ years relevant experience
- Experience with JavaScript, Java, or other object\-oriented programming language(s)
- Hands\-on experience and understanding of object\-oriented programming, data structures, algorithms, profiling \& optimization
- Passion for developing team\-oriented solutions to complex engineering problems
- Proficient in leveraging modern LLM tools to accelerate development workflows and enhance code quality
- This position requires the ability to obtain and maintain a \[LEVEL] security clearance. U.S. citizenship is required, as it is a condition of obtaining the clearance necessary for this role. Selected candidates must be able to successfully complete a U.S. Government background investigation
Benefits
CATHEXIS offers competitive compensation packages to all eligible employees. Our goal is to provide a compensation package that reflects the value you bring to our team, is competitive with national average market rates, and promotes your financial security and personal well\-being. The annual salary range for this role is $86,000\.00 \- $104,000\.00 Please note that the salary information provided is a general guideline. CATHEXIS considers various factors in its final offer, including location, qualifications, experience, and skills.
- Performance Bonuses
- Medical Insurance
- Dental Insurance
- Vision Insurance
- 401(k) Plan (Traditional and ROTH)
- Life Insurance (Basic, Voluntary \& AD\&D)
- Paid Time Off
- 11 Federal Holidays
- Parental Leave
- Commute Benefits
- Short Term \& Long Term Disability
- Training \& Development
- Wellness Program
- Community Outreach Initiatives
*CATHEXIS is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability EEO IS THE LAW. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact the Recruiting Department [email protected]*
Salary Context
This $86K-$104K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Cathexis, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($95K) sits 47% below the category median. Disclosed range: $86K to $104K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Cathexis AI Hiring
Cathexis has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Redwood City, CA, US, Tysons, VA, US. Compensation range: $104K - $150K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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