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About This Role
General Information
Locations
- East Bay, CA
- San Francisco, CA
- Silicon Valley, CA
Business Function Data & analytics Date posted Wednesday, September 16, 2026 Ref # JBR0001493 Description and Requirements
Job Description Senior Principal, Data & AI Go-To-Market – NorCal Market
Please note:This role isnoteligiblefor100% remotework. Employees must live within a commutable distance of our NorCal office and must be willing to be on-site at the client and/or Slalom office when needed. This role could require travel to support the region between 10-20%.
Who You'll Work With
At Slalom, we combine personal connection with global scale to create environments where work is loved, and life is enriched. Our mission is to help organizations redefine possibilities, shape the future, and achieve transformative goals. This position will play a pivotal role in driving Data and Intelligence sales and fostering customer-centric go-to-market strategies.
As a Data & AI Go-to-Market Lead for NorCal, you will be responsible for building a high-performing go-to-market team and function while owning the growth strategy for Slalom’s data and intelligence offerings. You will cultivate an inclusive, entrepreneurial team culture that attracts and retains top talent in data and AI, and position Slalom as a trusted innovation partner across the NorCal and Bay Area.
What You’ll Do
- You’ll tailor and scale our national vision into a local force—where innovation, execution, and business relevance converge. At Slalom, you’ll lead with purpose—pairing human ingenuity with the power of data to transform how organizations operate, compete, and grow.
- You’ll collaborate with leading technology partners and Slalom client teams to identify high-impact opportunities, drive sales and revenue growth, and elevate our go-to-market presence.
- Slalom’s Data & Intelligence capability helps build the next generation of data-driven organizations by leveraging data and AI as strategic differentiators. We help clients shift mindsets, foster data-driven cultures, and build modern data and AI solutions that accelerate the creation of products, services, and experiences—driving measurable value for organizations, employees, and customers.
Capability Vision & Strategy
- Tailor regional or country-wide capability strategies to our NorCal market, considering technology sector client portfolios in SaaS, Search, Social Media, and Semiconductor markets.
- Drive Data and Intelligence sales initiatives by leveraging industry and market insights and aligning strategic priorities with customer needs.
Go-to-Market Approach
- Proactively create demand by seeking speaking opportunities at conferences and events to establish yourself and your team as Data and Intelligence capability thought leaders.
- Collaborate with Marketing and Operations to plan Slalom-led thought leadership events focused on Data and Intelligence solutions and client outcomes.
- Ignite awareness and generate demand for Data and Intelligence capabilities by engaging in campaigns, RFIs, RFPs, workshops, and POCs.
Business Development & Sales
- Be accountable for Data and Intelligence sales growth and managing the opportunity pipeline through to closure, ensuring alignment with market strategies.
- Partner with client partners and sales executives to identify and pursue Data and Intelligence-related opportunities.
- Serve as a Solution Lead or SME in the pursuit process, engaging broader Data & Intelligence capability team SMEs as needed to align with market strategy.
- Participate in multi-capability solutioning and client outcome-based selling motions with the go-to-market team.
People Development
- Support the development of a small team of direct reports with a focus on Data and Intelligence go-to-market activities.
- Provide feedback for performance management related to the capability team members involved in market pursuits and delivery.
Delivery Management
- Contribute to delivery in billable roles as a Delivery Solution Lead and/or in SME roles within the market's portfolio, with defined utilization targets.
What You’ll Bring
- Minimum 10+ years in leadership roles required. Prior consulting experience strongly preferred.
- Experience with managing and driving capability revenue objectives ranging from $2 million to $5 million.
- Previous data architecture, engineering, and strategy leadership experience, ideally delivering with our leading partners, including Snowflake, Databricks, AWS, Microsoft, Anthropic, OpenAI, Google, Dbt, Fivetran, Matillion, and Dataiku.
- Expertise in the technology sector, specifically with SaaS, semiconductor, search, and social media products and services.
- Experience cultivating strategic relationships and initiating actions to expand the sales pipeline, collaborating closely with sales teams and client partners to identify and generate new business opportunities.
- Ability to partner effectively across cross-functional teams and organizations to drive Data and Intelligence sales and advance customer-centric initiatives.
About Us
Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries collaborate with clients to bring powerful customer experiences, innovative ways of working, and new products and services to life. We are trusted by leaders across the Global 1000, many successful enterprise and mid-market companies, and 500+ public sector organizations to improve operations, drive growth, and create value. At Slalom, we believe that together, we can move faster, dream bigger, and build better tomorrows for all.
Compensation and Benefits
Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that include meaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, and short/long-term disability. We also offer yearly $350 reimbursement account for any well-being-related expenses, as well as discounted home, auto, and pet insurance.
Slalom is committed to fair and equitable compensation practices. We are open to hiring at two levels. For Senior Principal, the targeted base salary pay range for this role is $188,000 to $258,000. In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time.
We are committed to pay transparency and compliance with applicable laws. If you have questions or concerns about the pay range or other compensation information in this posting, please contact us at: [email protected].
EEO and Accommodations
Slalom is an equal opportunity employer and is committed to attracting, developing and retaining highly qualified talent who empower our innovative teams through unique perspectives and experiences. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Slalom will also consider qualified applications with criminal histories, consistent with legal requirements. Slalom welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team or contact [email protected] if you require accommodations during the interview process.
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Salary Context
This $188K-$258K range is above the 75th percentile 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 Slalom Consulting, 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 $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 ($223K) sits 5% above the category median. Disclosed range: $188K to $258K.
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.
Slalom Consulting AI Hiring
Slalom Consulting has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Houston, TX, US, Chicago, IL, US, CA, US. Compensation range: $142K - $280K.
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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