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About This Role
Overview
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*What You’ll Be Doing*
Cadmus is seeking a Technical Project Manager (TPM) with a passion for delivering high quality projects in a complex, fast paced environment to support a major commercial client in the automotive entertainment space. You will work with machine\-learning (ML), data science/Artificial Intelligence (DS/AI), data engineering, and analytics teams to deliver initiatives that support the enterprise’s data needs. You will function as a TPM authority and team leader, owning programs that span multiple teams and systems, improving delivery processes, and serving as a key liaison with program stakeholders. We are looking for someone who is results\-oriented and comfortable with facilitating engineering teams to predictably meet commitments and successfully deliver high quality products and features. Your success will be achieved through clear communication and collaboration among the team, removing impediments, enabling flexibility and rapid respond to change, and providing leadership grounded in trust, transparency, and kindness. *Who We Are*
Cadmus provides government, commercial, and other private organizations worldwide with technology\-empowered advisory and implementation services. We help our clients achieve their goals and drive lasting, impactful change by leveraging transformative digital solutions and unparalleled expertise across domains.
At Cadmus, we look for team players and problem solvers who are driven to use their unique perspectives and intellectual curiosity to help deliver breakthrough solutions that achieve transformative goals. As a member of our team, you'll collaborate with leading experts to support our clients across the globe. We offer competitive compensation, outstanding health care and retirement benefits, a vibrant and collaborative work environment, and ample opportunities for professional growth.
Join Cadmus. Together, we are strengthening society and the natural world. For more information, visit cadmusgroup.com.
Responsibilities
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- Manage and coordinate across multiple engineering teams delivering Machine Learning (ML) and Data Science/Artificial Intelligence (DS/AI) features and capabilities to support the broader vision, goals, and objectives of the company. Coordinate activities with internal teams and external partners.
- Oversee end\-to\-end activities to ensure coordination and on\-time delivery. Build integrated schedules based on an understanding team’s activities and interdependencies between teams. Manage removing blockers, risks, and issues. Report program status.
- Lead Agile ceremonies and assist with story definition, backlog prioritization, and dependency resolution.
- Help Engineering teams balance scope, timeline, and quality to achieve optimal outcomes based on an understanding of priorities.
- Advocate for clear priorities, technical excellence, process and best practices adherence, and customer\-focused outcomes.
- Influence without authority and drive consensus across diverse stakeholders.
Qualifications
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- 5\+ years of experience as a Technical Project Manager or Scrum Master, managing cross\-functional projects.
- Bachelor’s degree in Information Systems, BI or Analytics or Engineering.
- Strong technical proficiency and experience working with Machine Learning (ML) or Data Science/Artificial Intelligence (DS/AI) teams.
- Experience managing large, ambiguous, cross\-functional programs operating in fast\-paced, highly collaborative environments.
- Experience performing TPM best practices (e.g., schedule development and tracking, risk management, status reporting).
- Experience regularly maintaining and reporting program data, preferably in Jira.
- Deep understanding of Agile and SDLC methodologies and leading/modeling process adherence.
- Excellent communication and problem\-solving skills.
- Ability to facilitate teams and individuals working collaboratively and efficiently.
- A composed presence, business acumen, and ability to communicate at multiple levels of the organization.
- Experience with automation and/or AI tools such as ChatGPT, Gemini, etc.
- Preferred certifications: PMP or Scrum Master.
Additional Information:
Candidates must be eligible to work in the United States as a U.S Perm Resident or U.S. Citizen.
Based on eligibility and job status, Cadmus offers an excellent benefits package to include: medical, dental, vision, company paid disability and life insurance, 401(k) program, paid time off (PTO), paid holidays, voluntary time off (VTO), tuition reimbursement, adoption assistance program, other optional benefits and various bonus programs.
The salary range for this position is $105,000 \- $115,000\. The actual salary will be determined by several factors, including relevant work experience, education, skills, and market competitiveness.
We value the critical role safety and health protocols contribute to everyone’s success at Cadmus, and work together to align and comply with all federal, state, and local safety and health mandates to ensure a safe and valuable work environment.
Cadmus is an Equal Opportunity Employer and prohibits unlawful discrimination. Cadmus is committed to providing a respectful workplace where equal employment opportunities are available to all applicants and employees without regard to race, color, religion, sex (including pregnancy), sexual orientation (including gender identity and/or expression), national origin, military and veteran status, physical and mental disability, or any other characteristic protected by applicable law.
Learn more about Cadmus by visiting our website at: cadmusgroup.com
Salary Context
This $105K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Cadmus Group, Inc., 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. This role's midpoint ($110K) sits 49% below the category median. Disclosed range: $105K to $115K.
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.
The Cadmus Group, Inc. AI Hiring
The Cadmus Group, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $115K - $115K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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