Sales engineers
Also called Solutions engineer, Pre-sales engineer, Pre-sales solutions engineer, Solutions architect pre-sales, Technical sales engineer
In brief
In May 2025, BLS counted 51,790 US sales engineers, with median pay of $124,900 a year. Of the BLS counts since 2003, the highest was 78,030, in May 2008. The earliest figure here is an estimate of 25,000 from 1922, not measured the same way as today's count. The last full technology era for sales engineers: Revenue intelligence platforms, 2015 to 2022.
What the sources say
Official outlook
BLS projects employment to grow 2.8% from 2025 to 2035.
AI exposure studies
- 53.4% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.
Last updated .
Cite this page
Future History. (2026, October 11). Sales engineers: jobs and pay since 1922 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/41-9031
Future History. "Sales engineers: jobs and pay since 1922." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/41-9031.
@misc{futurehistory_41_9031, author = {{Future History}}, title = {Sales engineers: jobs and pay since 1922}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/41-9031}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }
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≈ $121,698 in 2024 prices (derived by us, CPI-adjusted)
John Henry Patterson acquires James Ritty's mechanical cash register patents and renames the business the National Cash Register Company. Patterson builds a company sales school -- one of the first in American business history -- and trains his salesmen not only to close deals but to demonstrate the register's mechanical operation and explain its accounting logic. The requirement that salesmen understand how the product worked, not just what it cost, is the founding act of the Sales Engineer profession in the United States.
Harlow S. Person, dean emeritus of Dartmouth's Amos Tuck School and secretary of the Taylor Society, begins delivering lectures in which he applies Frederick Taylor's scientific management principles to the sales function. Person coins the term "sales engineering" to describe a systematic, analytically grounded approach to selling complex industrial products. The phrase enters professional vocabulary and begins appearing in trade journals and company titles. This is the earliest documented use of "sales engineering" as a named professional category.
IBM announces the System/360 family of mainframe computers in April 1964, a product so architecturally complex that IBM creates a new category of employee -- Systems Engineers -- to support its commercial sales force. IBM's Systems Engineers are distinct from salespeople: they understand operating systems, programming languages, and data center integration, and they carry the technical credibility required to persuade a buyer's IT team to commit to an IBM architecture. The IBM SE model becomes the industry template for technical pre-sales: one salesperson plus one dedicated SE as the standard enterprise deal team structure.
Salesforce is founded by Marc Benioff in San Francisco and launches the first commercially successful cloud-delivered CRM. The SaaS subscription model that Salesforce pioneered changes the SE's role in one structural way: because the buyer can access a trial instance of the software without buying it first, the SE's job shifts from "prove the product works" to "prove it works for your specific business process in your specific environment." This shift drives the development of personalized demo environments, proof-of-concept (POC) architectures, and custom integration trials as standard SE deliverables -- the modern enterprise SE playbook.
BLS introduces the 2000 Standard Occupational Classification, which creates a clean, standalone definition for Sales Engineers (SOC 41-9031): workers who "sell business goods or services, the selling of which requires a technical background equivalent to a baccalaureate degree in engineering." This distinguishes SEs from Wholesale and Manufacturing Sales Representatives (41-4011 / 41-4012), who may also sell technical products but without the engineering-depth threshold. The clean SOC definition enables the first reliable employment time series for the occupation.
Gong.io is founded (later launches its AI-powered conversation intelligence platform commercially in 2016-2017). Gong applies NLP and machine learning to recorded sales calls, generating automatic summaries, competitive mention tracking, and deal-risk signals from SE and AE call transcripts. It represents the first purpose-built AI tool for the SE profession and marks the beginning of the current AI-enabled SE tool era. By 2025 Gong reports adoption by over 4,000 customers globally, with revenue intelligence platforms used by a majority of enterprise SE teams.
Will AI replace sales engineers? What the research says
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.
What's changing day to day for sales engineers
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is taking this onDevelop and maintain sales forecasting reports and deal-stage documentation for the SE pipeline: log proof-activity outcomes, update technical evaluation status in the CRM, and provide forecast input on which deals have cleared the technical gate, a largely administrative task now substantially automated by AI-CRM integrations.
Develop and maintain sales forecasting reports and deal-stage documentation for the SE pipeline: log proof-activity outcomes, update technical evaluation status in the CRM, and provide forecast input on which deals have cleared the technical gate, a largely administrative task now substantially automated by AI-CRM integrations.[2],[4]
Let AI generate the pipeline snapshot: Salesforce Agentforce and Gong handle CRM logging, call-to-field mapping, and forecast roll-up automatically for teams running these integrations. Redirect the recovered time to deeper customer research and POC engineering. Your value in the forecast conversation is qualitative: "the IT Director has a hard compliance gate that will delay this 6 weeks" is context that is not in the CRM and cannot be generated by an AI forecast model. Build the discipline of adding qualitative technical-gate notes to CRM deals immediately after key discovery and evaluation calls.
AI is sitting alongside you hereRespond to Requests for Proposals (RFPs), Requests for Information (RFIs), and security questionnaires: translate buyer technical requirements and compliance questions into precise, accurate product capability statements, a task that previously consumed 8–12 hours per response but is now substantially accelerated by AI drafting from an internal knowledge base.
Respond to Requests for Proposals (RFPs), Requests for Information (RFIs), and security questionnaires: translate buyer technical requirements and compliance questions into precise, accurate product capability statements, a task that previously consumed 8–12 hours per response but is now substantially accelerated by AI drafting from an internal knowledge base.[9],[2],[10]
AI-generated RFP first drafts are now table stakes: the SE's value is in the review layer: catching inaccuracies, flagging over-promises, and adding the specific integration or architecture detail that only someone who has done live implementations actually knows. Build a curated, version-controlled internal knowledge base of accurate "gold responses" for your highest-frequency questions (security, data residency, SLA, API capabilities) and use Highspot or a ChatGPT custom GPT trained on this knowledge to generate drafts. Reps who review AI-generated RFP drafts in 30 minutes instead of writing from scratch in 8 hours are covering 5× more opportunities: the SE who cannot delegate this layer to AI is falling behind on capacity.
AI is sitting alongside you hereBuild and maintain competitive technical battlecards: research competitor products' technical architecture, integration limitations, security certifications, and known weaknesses, then train the AE team to use this intelligence to handle technical objections raised by the buyer's evaluation team during a bake-off.
Build and maintain competitive technical battlecards: research competitor products' technical architecture, integration limitations, security certifications, and known weaknesses, then train the AE team to use this intelligence to handle technical objections raised by the buyer's evaluation team during a bake-off.[9],[5],[2]
Competitive intelligence research is now heavily AI-acceleratable: use ChatGPT to synthesize competitor product documentation, G2/Gartner Peer Insights reviews, security certification databases, and LinkedIn engineering job postings into a structured battlecard first draft in hours rather than days. Use Gong to identify which competitor objections actually appear in your recorded discovery calls: prioritize battlecard depth on the objections that surface in real deals, not hypothetical ones. Highspot delivers the relevant battlecard automatically in a live call context. The SE's irreplaceable contribution is technical accuracy: AI can draft, but only someone who has run competitive bake-offs knows where the edges are.
Where sales engineers go next
Natural next steps for someone with your foundation: not exits, evolutions.
- Sales Managers
- US median pay
- $148,270 (May 2025)
- BLS outlook 2025-35
- +4.5%
- Computer and Information Systems Managers
- US median pay
- $175,140 (May 2025)
- BLS outlook 2025-35
- +15.8%
- Computer Systems Engineers/ArchitectsNo timeline for this role yet
What each move takes
Sales Managers
Senior Sales Engineers with strong AE collaboration track records and an interest in building and coaching teams are well-positioned for SE management or broader sales management roles. The transition is relatively accessible because SEs already operate at the deal-strategy level: the additional skill delta is in quota management, hiring, performance management, and organizational leadership rather than domain knowledge. Pavilion (2025) finds SE managers commanding $200k–$280k OTE in enterprise software, materially higher than individual-contributor SE compensation, making this a financially compelling path for SEs with 5+ years of top-performance. The SE-to-Sales-Manager path via SE management (managing a team of SEs) is the most natural route, preserving technical credibility while building people-management experience.
- · SE team management: capacity planning, deal-coverage models, and SE performance metrics (technical win rate, POC conversion rate, SE-influenced ARR)
- · Hiring and interview design: technical evaluation rubrics for SE candidates; assessing product knowledge depth, communication skills, and discovery capability simultaneously
- · Quota and compensation design: SE incentive plan mechanics; balancing deal-support accountability with capacity constraints across a team
- · Forecast ownership: managing upward to VP of Sales on technical win likelihood and SE resource needs per deal cycle
- · Coaching methodology: structured call-review frameworks using Gong; identifying and correcting the SE-specific failure patterns (over-demo'ing, under-qualifying, poor POC scoping)
How has the work of sales engineers changed since 1890?
The tools that defined the work, one line per era. Select an era to see how it reshaped the work.
Physical product demonstration (NCR era: mechanical register, demonstration kits, sales primers)
The Sales Engineer of the 1890s and 1900s traveled with the product itself or with a scaled demonstration model. NCR salesmen carried demonstration cash registers and were trained to perform a three-part presentation: approach, demonstration, close. John Henry Patterson's 1894 "NCR Sales Bible" was the first formalized technical-selling methodology in American business history. At Westinghouse and GE, field engineers traveled to factories and utilities with portable demonstration equipment and performed load calculations on site to prove that electrical machinery would meet the buyer's specifications. The entire toolset was physical: the product, the demonstration, and the engineer's own technical memory.
Effect on the workNCR built a field sales force of over 4,000 by 1910, making it one of the largest technical sales organizations in the world. The productivity of this force -- demonstrated products closing at higher rates than verbal-only pitches -- validated the model and drove competitors in every capital-equipment sector to build their own technical sales teams.
Sales engineering as a formal discipline (Harlow S. Person's framework + industrial application engineering)
Harlow S. Person's 1922 lectures introduced the phrase "sales engineering" into professional vocabulary and gave the practice an intellectual framework: apply Taylor's scientific management to the selling process, study the buyer's production problem analytically, specify the solution precisely, and demonstrate quantified outcomes. In chemical processing, petroleum, and heavy manufacturing, Application Engineers became standard roles: engineers who combined deep product knowledge with the ability to write specifications, run flow calculations, and present a business case to plant management. By the 1930s and 1940s, major industrial firms -- Caterpillar, Ingersoll-Rand, Carrier Corporation, Dow Chemical -- maintained field sales engineering forces of hundreds to thousands. The tools remained largely manual: slide rules for engineering calculations, specification binders, and the traveling demonstration kit.
IBM Systems Engineers + mainframe-era computing sales (System/360, 1964)
When IBM launched the System/360 family of mainframe computers in April 1964, it created a new class of internal specialists: "Systems Engineers," who provided pre-sales technical support to the commercial sales force. IBM's Systems Engineers (SEs) were distinct from the sales representatives: they understood programming, operating systems, and systems integration; they could answer the buyer's data center architects; and they managed the technical side of competitive evaluations against Burroughs, UNIVAC, Honeywell, and CDC. IBM's branch offices in the 1960s and 1970s were organized around this SE model -- sales rep plus SE as a team -- and IBM trained its SE force at its dedicated school, running intensive multi-week programs that became the industry template. The model spread rapidly: Xerox, Digital Equipment Corporation, Hewlett-Packard, Amdahl, and later Oracle and Sybase all built SE organizations modeled on the IBM structure. The toolset was still largely paper-based: demo scripts, technical specification sheets, and hand-prepared benchmark proposals.
Effect on the workThe mainframe era drove the first major wave of professional SE employment growth. IBM alone had tens of thousands of employees in its branch office SE structure by the 1970s. The model of "sales rep + dedicated technical SE" became the standard B2B go-to-market pattern for complex technology products and persists as the dominant structure today.
Enterprise software SE model (Oracle, SAP, Siebel; laptop-era demonstrations)
The PC revolution commoditized hardware, but enterprise software vendors created a new and larger demand for Sales Engineers. Oracle's aggressive growth in the late 1980s and early 1990s under Larry Ellison relied heavily on a technical pre-sales organization: SEs who could run SQL queries against live Oracle databases in front of the buyer's IT team, demonstrate integration with existing applications, and position Oracle's relational database architecture against IBM DB2, Sybase, and Informix. SAP's R/3 (launched 1992) was vastly more complex than any prior business application and required SEs who understood materials requirements planning, financial integration, and manufacturing workflows -- not just SQL. Siebel Systems (CRM, founded 1993) built one of the largest per-capita SE organizations in the industry. The laptop made the portable live-product demonstration possible for the first time: SEs could now run the actual software on their own hardware rather than relying on a mainframe terminal session.
Effect on the workEnterprise software SE employment grew rapidly in the 1990s dot-com era. The competitive intensity of the Oracle/SAP/Siebel/PeopleSoft bake-off cycle made technical differentiation the primary purchase criterion at the enterprise level, which meant SE headcount tracked software company revenue growth almost linearly.
SaaS and cloud-era pre-sales (Salesforce, Workday, ServiceNow; POC environments, CRM-integrated selling)
Salesforce's AppExchange and subscription model (from 1999) changed the SE's role in one important way: the buyer could try the product in a free trial, which meant the SE's job shifted from "demonstrate the product exists and works" to "demonstrate that it works for your specific business process in your specific environment." This drove the emergence of personalized demo environments, proof-of-concept (POC) architectures, and sandbox integration testing as core SE deliverables. The cloud model also shortened deal cycles in SMB while making enterprise deal cycles more complex (security reviews, data governance, compliance checks, integration audits became standard). Sales analytics tools -- initially Salesforce itself, then dedicated SE tools like Highspot and Gong -- gave SE managers visibility into demo engagement, RFP win rates, and deal-stage technical gates for the first time.
Revenue intelligence platforms (Gong, Highspot, Seismic; RFP automation, enablement layers)
The 2015-2022 period brought the first AI-adjacent tooling purpose-built for Sales Engineers: call recording and conversation intelligence (Gong, Chorus), sales enablement and content management (Highspot, Seismic), and interactive demo platforms (Walnut, Reprise, Navattic). Gong launched in 2015 and began applying NLP to recorded sales calls by 2017; by 2019 it was reporting on competitive mention rates, talk-to-listen ratios, and deal risk signals from call transcripts. Highspot gave SE teams a searchable content library and began surfacing the right battlecard or integration guide contextually during active deals. These tools shifted SE time away from content assembly (finding the right slide deck, writing the proposal from scratch) toward review, customization, and live engagement -- a meaningfully better use of technical expertise.
Generative AI and demo automation (ChatGPT, Claude, GitHub Copilot, Consensus, Walnut; AI-first SE stack)
The 2023-2026 period is the most significant tool transition in the SE profession since the laptop made portable live demos possible in the 1990s. Three changes are happening simultaneously. First, AI-generated first-draft RFP and RFI responses (using Highspot AI, ChatGPT, or Claude against an internal knowledge base) are substantially cutting turnaround time per Highspot's 2025 State of Sales Enablement report, freeing SE capacity for high-value technical work. Second, demo automation platforms (Consensus, Walnut, Reprise) let SEs build self-service interactive demo flows that prospects consume asynchronously; Consensus's 2025 SE research finds over half of organizations are automating some demo processes, with measurable reductions in unqualified live demos. Third, AI coding assistants (GitHub Copilot, Cursor) are accelerating custom POC integration engineering, with GitHub's own research finding developers complete coding tasks up to 55% faster with AI assistance. The net effect is not displacement but capacity multiplication: SEs who adopt the full AI stack can cover materially more enterprise accounts at higher quality, which makes them more valuable to their employers and harder to displace.
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Browse all rolesSales Engineers employment and pay by year
View all 28 years of cited US data, each linked to its source
| Year | US employment | Annual pay (median unless marked) | Source | Report |
|---|---|---|---|---|
| 1922 | 25,000 | n/a | Est., Harlow S. Person Researched context | Report |
| 1956 | n/a | $6,200 | Est., University of Missouri Libraries Researched context | Report |
| 1960 | 70,000 | n/a | Est., IBM in the Computer Era Researched context | Report |
| 1990 | 45,000 | n/a | Est., O*NET 41-9031.00 Researched context | Report |
| 2000 | 88,240 | $56,520 | BLS OEWS 2000 Official statistic | Report |
| 2003 | 73,200 | $67,790 | BLS OEWS May 2003 Official statistic | Report |
| 2004 | 71,690 | $70,620 | BLS OEWS May 2004 Official statistic | Report |
| 2005 | 69,790 | $74,200 | BLS OEWS May 2005 Official statistic | Report |
| 2006 | 75,150 | $77,720 | BLS OEWS May 2006 Official statistic | Report |
| 2007 | 75,940 | $80,270 | BLS OEWS May 2007 Official statistic | Report |
| 2008 | 78,030 | $83,100 | BLS OEWS May 2008 Official statistic | Report |
| 2009 | 71,640 | $83,190 | BLS OEWS May 2009 Official statistic | Report |
| 2010 | 66,060 | $87,390 | BLS OEWS May 2010 Official statistic | Report |
| 2011 | 65,580 | $89,330 | BLS OEWS May 2011 Official statistic | Report |
| 2012 | 65,410 | $91,830 | BLS OEWS May 2012 Official statistic | Report |
| 2013 | 65,730 | $93,900 | BLS OEWS May 2013 Official statistic | Report |
| 2014 | 68,080 | $96,340 | BLS OEWS May 2014 Official statistic | Report |
| 2015 | 72,200 | $97,650 | BLS OEWS May 2015 Official statistic | Report |
| 2016 | 74,330 | $100,000 | BLS OEWS May 2016 Official statistic | Report |
| 2017 | 70,820 | $98,720 | BLS OEWS May 2017 Official statistic | Report |
| 2018 | 65,720 | $101,420 | BLS OEWS May 2018 Official statistic | Report |
| 2019 | 63,550 | $103,900 | BLS OEWS May 2019 Official statistic | Report |
| 2020 | 63,780 | $108,830 | BLS OEWS May 2020 Official statistic | Report |
| 2021 | 59,550 | $103,710 | BLS OEWS May 2021 Official statistic | Report |
| 2022 | 59,700 | $108,530 | BLS OEWS May 2022 Official statistic | Report |
| 2023 | 59,340 | $116,950 | BLS OEWS May 2023 Official statistic | Report |
| 2024 | 56,690 | $121,520 | BLS OEWS May 2024 Official statistic | Report |
| 2025 | 51,790 | $124,900 | BLS OEWS May 2025 Official statistic | Report |
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