Interested in this AI/ML Engineer role at CSC Generation?
Apply Now →About This Role
CSC Generation is the AI\-native holding company re\-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B\+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real\-world innovation labs.
Reports to: Corporate Controller
Location: Remote US / Remote Canada
### About the Role
CSC Generation is building an AI\-native accounting function from the ground up — software agents, automated reconciliations, and compressed close cycles are the mandate, not an aspiration. As Director of Accounting, AI\-Operations, you will own all aspects of accounting operations across a portfolio of 13 brands while designing and deploying the intelligent workflows that replace manual processes.
We are open to two profiles: a Senior Manager ready to step into a Controller seat and prove themselves through AI\-driven execution, or an experienced Controller who is genuinely deep on AI and can demonstrate it. Either way, you will be building automations — not just overseeing them. In your first year, success means a measurably faster close, fewer manual reconciliation touchpoints, and an accounting center of excellence that other portfolio companies adopt.
This is a lean team. You will own a lot, move fast, and make decisions with full end\-to\-end responsibility.
### What You'll Do
#### Own the Accounting Foundation
- Own all aspects of accounting operations — monthly close, reconciliations, and monthly financial reporting across multiple portfolio companies.
- Manage the general ledger, ensuring accuracy, consistency, and compliance with GAAP and ASC 606\.
- Oversee revenue recognition, contract setup, and billing integrations (Stripe and similar platforms).
- Operate at the corporate holding company level with the ability to get into the weeds with individual portfolio company general ledger systems and processes.
#### Build Intelligent Workflows and an Accounting Center of Excellence
- Design and deploy AI agents for A/R, A/P, and General Ledger reconciliations — eliminating manual touchpoints and compressing close timelines.
- Evaluate and implement automation tools that improve close speed, accuracy, and financial visibility.
#### Cross\-Functional and Portfolio Support
- Support tax compliance activities as needed.
- Support annual audits and the annual budget process as needed.
- Backfill portfolio\-company accounting positions as needed, providing hands\-on coverage where the business requires it.
### Required Qualifications
- CPA or equivalent experience managing audit processes and ASC 606 compliance.
- 5–8 years of progressive accounting experience in PE\-backed or high\-growth companies.
- Proven ownership of a financial close process — you have led it, improved it, and taken full accountability for results.
- Hands\-on AI experience you can speak to concretely: you have built automations, deployed agents, or materially accelerated workflows using AI tools.
- Track record of implementing and optimizing accounting systems, integrations, and workflow automations.
- Strong communicator who works fluidly across engineering, finance, and go\-to\-market teams.
- Comfortable operating in ambiguity and high growth, where you are often setting the process before you follow it.
### Why Join
The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
- Executive Access: Work directly with the Corporate Controller and C\-suite leadership, shaping how accounting operates across a $1B\+ portfolio.
- Build the Playbook: Define what an AI\-native accounting function looks like — your automations and workflows will become the standard across 13 brands.
- Portfolio\-Wide Ownership: Set strategy for accounting operations at the holding company level with direct line\-of\-sight into every brand.
- Competitive Benefits (US): Paid time off policies, 401(k) match, medical/dental/vision and a variety of supplemental policies, and employee discounts across our portfolio of brands.
- Competitive Benefits (Canada): Paid time off policies, RRSP match, medical, dental, and vision coverage, a variety of supplemental benefit options, and employee discounts across our portfolio of brands.
### Interview Process
- Recruiter Screen \- A 30\-minute conversation with our recruiting team to align on the role, your background, and what you are looking for.
- Hiring Manager Interview \- Conversation with the Corporate Controller focused on accounting operations depth, AI experience, and leadership approach.
- Technical / Case Discussion \- A deeper dive into your hands\-on AI and automation work — expect to walk through specific projects, tools, and outcomes.
- Executive Interview \- Meeting with senior finance leadership to assess strategic fit and alignment with portfolio\-wide priorities.
- In\-Person Interview \- We'd like to meet you at one of our offices (Salt Lake City, Austin, or Toronto). Details and logistics will be arranged with your recruiter.
- Reference Checks \- Conducted in parallel with the final stages where possible.
- Offer \- We move quickly for the right candidate.
*Interview process is subject to change. Any updates will be communicated promptly and clearly.*
CSC Generation is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law.
The CSC Generation family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact [email protected].
For Ontario applicants, please note that this posting is for an existing vacancy.
For US\-based candidates, this posting is intended for candidates that reside in the following states:
AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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 $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At CSC Generation, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($170K) sits 6% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CSC Generation AI Hiring
CSC Generation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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