Interested in this AI/ML Engineer role at CompassX Group?
Apply Now →About This Role
CompassX is a boutique business and technology consulting firm. We help Fortune 500 and high-growth clients deliver their most strategic initiatives, from enterprise transformations to digital and data-driven projects. With over 15 years of proven results, we’ve expanded across industries including financial services, pharmaceuticals, aerospace, consumer products, and quick service restaurants.
We are honored to be recognized as a three-time winner of Consulting Magazine’s Best Boutique Firms to Work For, and previously recognized as a “Best Place to Work” in Southern California and one of INC.’s 5000 fastest-growing private companies in the U.S.
We're looking for a Solution Owner (Data, Technology & AI) to be the face of CompassX on a complex, high-visibility client engagement. This isn't a traditional Project Manager role. We need someone who sits with client executives from the earliest conversations - while a solution is still just a problem statement - and stays with it through design, build, and adoption. You'll be trusted to shape what gets built, not just track when it ships.
The strongest candidates bring a rare combination: consulting-grade client presence and leadership, real solution design and architecture instincts, and enough technical fluency in data and AI to know when an approach is right - not just when a sprint is on schedule.
While we have a strong preference for candidates local to the Southern California (LA/OC) area, exceptional candidates based elsewhere within the United States will also be considered.
What You'll Do
Client Leadership & Communication
- Act as the primary, trusted face of CompassX to senior client stakeholders - building the kind of credibility that gets you invited into the room before a problem is even fully defined
- Lead executive-level conversations that turn ambiguous business challenges into a clear, actionable solution direction
- Build consensus across stakeholder groups with competing priorities, and know how to navigate organizational dynamics without losing momentum
Solution Design & Ownership
- Own the design of the solution itself - not just the plan to deliver it. Shape the approach, validate it technically, and defend it under scrutiny
- Partner closely with architects, engineers, and data/AI practitioners to ensure the technical approach holds up, while remaining the accountable owner of the overall solution
- Drive process improvement and "ways of working" recommendations as part of the solution, not as an afterthought
Data, Technology & AI Fluency
- Evaluate the feasibility of data and AI use cases, and speak credibly to data pipelines, model approaches, and modern architecture
- Bring a point of view on where AI genuinely adds value to a solution - and where it doesn't
- Use AI as part of your own working style (research, synthesis, solutioning), modeling the kind of modern practice CompassX brings to clients
Delivery (supporting, not core)
- Bring enough fluency in delivery fundamentals - Agile, UAT, data validation - to keep a workstream credible, with support from delivery specialists as needed
- This is not a scrum-master or backlog-management role; delivery mechanics are a "nice to have," not the job
What You'll Bring
- 8+ years of experience spanning consulting, product ownership, and/or solution design — ideally with time at a reputable consulting firm
- A track record of being the trusted senior presence in front of executive clients, not just a coordinator of workstreams
- Real experience shaping or designing a solution (architecture, process, or product), not only managing its delivery
- Working knowledge of data and AI environments — pipelines, analytics, model evaluation, or modern data architecture
- Strong executive communication and consensus-building skills
- Bachelor's degree or higher (Business, Computer Science, Engineering) or equivalent demonstrable experience
- Ideally based in Southern California
$130,000 - $180,000 a year The posted compensation range reflects the anticipated compensation for this opportunity. Depending on the role, client requirements, and business needs, compensation may be structured as a salaried position with bonus eligibility or as an hourly contract engagement (including 1099 independent contractor arrangements). Final compensation and engagement type will be determined based on factors including experience, skills, qualifications, internal equity, market conditions, and the nature of the role.
Our consultants say CompassX is a place where they can truly find their own path. As part of a smaller firm, they see the impact of their work, have an identity, and know their voice matters. They value the freedom to take the reins of their careers, shape their client work, and maintain a direct line to leadership.
With CompassX’s focus on serving local Southern California clients, consultants not only build lasting relationships but also see the results of their work in the communities where they live.
At CompassX, you won’t just follow a path - you’ll create one. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $130K-$180K 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 CompassX Group, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $130K to $180K.
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.
CompassX Group AI Hiring
CompassX Group has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $160K - $190K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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