Job Seeking
Start with what you would sell and who would buy it—not the AI label. Separate developer-facing models and APIs, ML platforms, and applications built for a particular workflow. Ask whether the customer purchases usage, seats, a subscription or a negotiated commitment. That distinction helps you assess your experience against the job.
Map the buying group: a business leader may own the outcome, data scientists may evaluate performance, engineering may assess integration, and security, legal or procurement may review the purchase. Investigate the actual sequence rather than assuming every deal follows it.
For any role, ask how discovery becomes an evaluation, how an evaluation becomes a contract, and what can delay production deployment.
Search by role and product category together: “AI platform account executive,” “machine learning SDR,” “enterprise AI account manager,” “MLOps sales” and “document automation sales.” Broaden beyond companies that put AI in their name. You can browse sales jobs on Salesfolks and compare descriptions against your target role.
Build a shortlist with columns for product, buyer, customer segment, territory, location requirements and evidence still needed. Check each employer’s own materials for product positioning and open-role details; confirm anything unclear with the recruiter.
For United States roles, verify eligible work locations, time-zone expectations, travel and territory coverage. “Remote” alone does not answer those questions. Favor industries where you can discuss real workflows—for example, insurance document review—without pretending to have technical or regulatory expertise you lack.
Make your resume auditable. Separate personal results from team results, state the period and denominator behind attainment figures, and distinguish sourced pipeline from closed revenue. If you have not sold AI products, explain transferable experience with technical evaluations, multiple stakeholders or complex procurement.
Prepare a two-page sales plan for a hypothetical document-classification pilot. Use fictional information, not a former employer’s customer documents.
Label this as a proposal, not a completed customer success. A demonstration is not a validated production result. Do not invent accuracy, revenue gains or deployment readiness.
Ask for base salary, target variable compensation, quota, payout rules and ramp terms separately. Treat on-target earnings as a conditional calculation, not guaranteed income. Clarify whether a draw is recoverable, when commissions are earned and paid, and what happens after cancellations or territory changes.
For pay research, use BLS occupational wage tables. Select the occupation that best matches the duties, then the relevant geography and available year. Use O*NET to help compare tasks and skills. Do not treat a broad national occupation as a local AI-sales salary quote.
Ask for concrete examples while respecting customer confidentiality. A useful answer explains the process, ownership and tradeoffs—not just the product vision.
Ask how long recent comparable deals took, where they stalled, and whether an evaluation can end without a production purchase. Compare answers across the recruiter, manager and a prospective peer.
Be cautious when a team cannot distinguish a polished demonstration from evidence gathered under a customer’s operating conditions. Ask what remains untested and who owns validation. If a role expects you to promise performance before technical review, clarify your authority to decline unsupported commitments.
Uncertainty is not automatically disqualifying. What matters is whether the employer acknowledges it and provides a credible way to investigate.
Use the same checklist for every offer. Mark each item confirmed, unclear or unacceptable, and record who supplied the answer. Decide which conditions are non-negotiable before comparing headline compensation.
Hypothetical comparison: Offer A advertises higher target earnings but leaves account ownership and pilot credit unclear. Offer B advertises lower target earnings and supplies a defined territory, written crediting rules and scheduled coaching. Neither is automatically better. Resolve A’s uncertainties, then weigh both against your financial needs, experience and tolerance for ambiguity.
Check the role’s actual requirements. Ask whether you must build demonstrations or simply coordinate technical specialists. Prepare to discuss the customer workflow and your own technical limits honestly.
Make the case through comparable buyers, procurement complexity and evaluation experience. A focused work sample is more credible than relabeling unrelated experience as AI expertise.
Ask what could go wrong, who evaluates it and when specialists become involved. Avoid offering technical, legal or security assurances outside your competence.
The NIST AI Risk Management Framework offers voluntary guidance for considering trustworthiness when developing, using and evaluating AI systems. Use it as background for questions about evaluation ownership, not as proof that an employer or product is trustworthy.
Verify current compensation benchmarks through BLS and role tasks through O*NET using the links above. Confirm changing job requirements, compensation plans and work-location rules directly with each employer. Next, browse sales jobs or create a Salesfolks account, then apply your shortlist and interview checklist.
Employer perspective: Hiring AI and Machine Learning Salespeople: A Practical Employer Guide.