Interested in this AI/ML Engineer role at Sur La Table?
Apply Now →Skills & Technologies
About This Role
With over 59 stores and the largest avocational cooking program in the US, Sur La Table offers an unsurpassed selection of exclusive and premium\-quality goods for the kitchen and table – and the culinary expertise and inspiration to go along with it. Whether the job entails interacting with our customers, driving digital growth, or providing vital behind\-the\-scenes support, we’re all here for the same reason – to roll up our sleeves and create happiness through cooking and sharing good food.
The Director, FP\&A and AI Finance plays a central role in Sur La Table’s success by delivering strategic financial insights and supporting sound investment decisions across the business. This role leads the budget and forecast processes, drives key financial analysis and reporting, and manages a high\-performing FP\&A team.
The Director of FP\&A reports directly to the division Chief Financial Officer.
What you get to do:
- Drive annual budget process across all channels and departments.
- Lead the monthly forecast process, proactively identifying forecast opportunities and risks.
- Lead AI\-driven process transformation across the FP\&A function \— prototyping and deploying automated workflows, agent\-based solutions, and always\-on reporting tools that reduce manual work and accelerate insight delivery.
- Develop strong partnerships with business leaders to understand key operational drivers and deliver clear, actionable financial insights.
- Partner with Business Development to provide financial modeling and analysis on new business opportunities.
- Support fleet management with financial analysis, including ROIC analysis on capital investments.
- Leverage technology to evaluate and extract data that informs decision\-making.
- Build, lead, and develop an efficient, high\-performing FP\&A team.
What you bring:
- A Bachelor’s degree in Finance or a related field; MBA and/or CPA preferred.
- 10\+ years of progressively responsible FP\&A experience.
- Hands\-on AI experience applied within finance workflows \— not just using AI to polish emails, but concrete wins such as building automated reporting pipelines, deploying agent\-based tools (e.g., n8n, Claude Code, ChatGPT), or creating custom solutions that replaced manual FP\&A processes. You can walk us through exactly what you built and the business impact it had.
- Strong command of corporate finance and accounting principles, with the ability to translate complex concepts clearly for non\-financial audiences. You move comfortably between the P\&L, Balance Sheet, and Statement of Cash Flows and understand how they interact.
- An advanced proficiency with Microsoft Office (Excel, Word, Outlook, PowerPoint).
- Direct FP\&A experience in an omnichannel retail environment \— supporting both physical store and ecommerce P\&Ls. Candidates without meaningful retail finance experience are unlikely to hit the ground running in this role.
- Analytical and quantitative at your core \— you've built complex financial models from scratch and can bring financial dimension to ambiguous problems.
- A track record of building and developing high\-performing teams, with strong workflow management and the ability to keep deliverables on track across a team.
- You are accurate and are able to put processes in place to ensure that financial information is presented correctly across your team.
- Excellent presentation and communication skills, with experience leading larger group meetings and presenting to senior leadership.
- You are intensely curious and have a proactive approach, digging deep to understanding business processes, to uncover solutions to problems, and to connect financial information to the operations of the business.
- Multiple budget cycles under your belt, with clear perspective on what works, what doesn't, and how to guide an organization through the process while keeping it on schedule.
- You are a player\-coach leader who is equally comfortable building models and synthesizing analysis as delegating and developing team members \— this role will have you rolling up your sleeves and producing work, not just overseeing it.
- You have developed key expertise in utilizing and improving financial planning and reporting systems.
- You have demonstrated the ability to influence and drive leadership decisions and tangible outcomes.
What’s in it for you? Joining CSC Generation isn’t just about having a seat at the table\—it’s about helping redesign the table entirely. You’ll be challenged, stretched, and supported as you grow faster than you thought possible. In addition to competitive compensation, we offer:
- Executive Access: Work directly with brand CEOs and senior leadership, solving real business problems and earning mentorship from top operators.
- AI\-First Skill Building: Get hands\-on with the most advanced AI tools in the market. From automation to prompt engineering, you’ll build a modern tech stack that sets you apart in any industry.
- Accelerated Career Path: High performers are quickly entrusted with greater responsibility, new challenges, and leadership opportunities across our portfolio of brands.
- Competitive benefits: Paid time off policies, 401(k)/RRSP match, medical/dental/vision and a variety of supplemental policies, and employee discounts at our portfolio companies.
What our interview process looks like:
- Step 1: If you align with our vision and meet the qualifications, we’ll reach out to schedule a conversation and introduce CSC.
- Step 2: You’ll complete a short AI or product\-building challenge so we can understand how you approach problems and execution.
- Step 3: Participate in deep\-dive interviews with CSC leadership focused on your experience, product mindset, and operational thinking.
- Step 4: Offer. We’ll move fast for the right candidate.
The CSC family of brands provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, provincial, state or local laws.
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.
Washington state applicants only: If you believe that this job posting does not comply with applicable Washington state law, please notify us by sending an email to WACandidates@cscshared.com.
It is unlawful in Massachusetts to require or administer a lie\-detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
The CSC family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or an accommodation due to a disability, please contact hrbenefits@cscshared.com.
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.
Work Location: Remote
Salary Context
This $160K-$175K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Sur La Table, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. Disclosed range: $160K to $175K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Sur La Table AI Hiring
Sur La Table has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $175K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.