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About This Role
*To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.*
Job Category
Sales
Job Details
About Salesforce
Salesforce is the \#1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level\-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
Applications will be accepted until 07/31/2026\.
We are hiring for the role of Lead or Principal Account Solution Engineer for OpenAI within our Strategic Technology segment.
Overview
Do you want to be part of an outstanding Solution Engineering team that works with and innovates alongside the world's top technology companies? Come join us! Through collaboration with customers and internal stakeholders, Account Solution Engineers develop thoughtful solutions that help clients with their biggest challenges. If you enjoy a fast\-paced environment full of opportunities to build compelling solutions, then the Solution Engineering team is the place for you.
For this role, the Account Solution Engineer must have extremely strong technical acumen across a broad swath of Salesforce solutions and deep expertise in Generative AI. This Solution Engineer will also need to demonstrate honed discovery expertise and the ability to take their discoveries and provide value quickly and holistically to their customers. We are looking for a high performing individual with a demonstrated track record of creativity in customer interactions and solution design, as well as a passion for Generative AI.
Responsibilities
- Strategic Account Partnership : Serve as a main technical voice for your account team while working with one of the world's most sophisticated AI\-native organizations. Build deep, trusted relationships with C\-suite, engineering leadership, and technical stakeholders, understanding OpenAI's unique operating model and translating that into high\-impact Salesforce solutions.
- Platform and Data Expertise : Demonstrate how Salesforce's holistic platform capabilities complement and integrate with OpenAI's own models and infrastructure.
- Business Problem Solving with an AI\-First Lens : Take ambiguous, complex business problems and apply an AI\-first mindset to define the challenge, identify opportunities, and recommend practical, scalable solutions — often in real time in front of highly technical audiences.
- Innovation Management : Guide customers through solution evaluation and analysis, aligning stakeholders and co\-developing new frameworks that deliver lasting value well beyond any single engagement.
- Technical Discovery \& Architecture : Conduct deep\-dive discovery with engineering, IT, and business teams to uncover core challenges and architectural requirements. Convey a sophisticated understanding of your customer's technical ecosystem and articulate how Salesforce solutions integrate at scale. Lead the discovery, analysis, and recommendation of strategic solutions. Deliver transformational pitches grounded in live product demonstrations that validate the business case and path to value using the full Salesforce platform.
- Executive\-Level Demonstrations \& Prototyping : Be prepared to design and deliver compelling, customized product demonstrations and rapid prototypes — including live demos — tailored to a wide range of technical, business, and executive personas.
- Cross\-Functional Orchestration : Orchestrate cross\-functional solution teams — including Specialist SEs, Architects, Data and AI experts, and more.
- Competitive Intelligence : Maintain a nuanced understanding of the current AI competitive landscape including major LLM providers and point solutions. Actively pursue Salesforce Certifications and AI\-related training to stay ahead of the rapidly evolving product landscape.
- Collaboration with Product Teams : Act as a feedback loop between OpenAI and Salesforce's Product organization. Surface gaps, influence roadmaps, and advocate for new, impactful functionality.
- Travel : Ability to travel based on account needs, key business reviews, and strategic engagements.
Required Qualifications:
- B.S. in Computer Science, Software Engineering, or MIS
- Deep, hands\-on expertise across the Salesforce platform
- Demonstrated expertise in Generative AI and LLM ecosystems including how enterprise AI is built, deployed, and governed at scale
- World\-class communication skills with the ability to command technical depth and executive presence in the same conversation
- Proven ability to thrive in high\-stakes, fast\-paced environments with ambiguous, complex problems
- Minimum 4 years of professional experience as a Solution Engineer, Salesforce Consultant, Technical Architect/Enterprise Architect focused on Salesforce solutions, or a related field
Preferred Qualifications
- Multiple Salesforce Certifications
- Experience working with AI\-native, high\-growth technology companies
- Background in or deep knowledge of the broader AI/LLM competitive landscape
Unleash Your Potential
When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and *be your best* , and our AI agents accelerate your impact so you can *do your best* . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non\-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job\-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.\&\#xa;\&\#xa;The typical base salary range for this position is $134,750 \- $210,910 annually\&\#xa;\&\#xa;There is a different range applicable to specific work locations. In California and New York, and select cities in the metropolitan areas of Boston, Chicago, Seattle, and Washington DC, the base pay range for this role in those locations is $148,190 \- $231,980 per year. Your recruiter can share more about the specific salary range for the job location during the hiring process.\&\#xa;\&\#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $134K-$231K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Salesforce, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $134K to $231K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Salesforce AI Hiring
Salesforce has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span McLean, VA, US, San Francisco, CA, US, Indianapolis, IN, US. Compensation range: $223K - $401K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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