Full-Time

Senior Technical Marketing Engineer

Posted on 8/15/2025

Sourcegraph

Sourcegraph

51-200 employees

AI-assisted code search, refactoring, and fixes

Compensation Overview

$158.5k - $180k/yr

+ IC3 Zone 1: 158,500 USD + IC4 Zone 1: 180,000 USD

San Francisco, CA, USA

In Person

Category
Growth & Marketing (3)
, ,
Required Skills
Sales
Marketing
Requirements
  • You enjoy writing and consider yourself a strong writer. Much of the work involves communicating ideas effectively in writing, so this skill is critical to the role.
  • You have some experience with code and building apps. You use AI coding assistants yourself and know the landscape and have your favorites. This could be writing code professionally, or familiarity through schoolwork or as a hobby.
  • You’re excited about a high-agency role. A lot of this role will be work that is done for the first time (for example, interviewing customers and sellers to compile research on new use cases for generative AI). There is lots of room for high-agency, strategic thinking, and creative approaches in this role.
  • You’ve worked in an environment with a product-led growth model.
  • You’re ready to roll up your sleeves and do both strategic and tactical work. As the Technical Marketing Engineer, you will do research and help with narrative storytelling, but you’ll spend even more time writing content, getting on calls with customers, and occasionally running webinars.
Responsibilities
  • Technical product content (videos & written content)
  • Product-specific support: Bi-weekly & monthly release GTM, Amp (shared)
  • Support customer marketing
  • Product-led growth (shared)
  • Content & thought leadership (shared)
  • Become an expert on our product, including its strengths and limitations.
  • Take over analyst relations, maintaining relationships with industry experts across top firms.
  • Begin researching new use cases for AI in the developer ecosystem, including through customer calls, to bring new insights back to the Marketing team.
  • Regularly join customer calls and calls with the Sales team.
  • Report learnings and insights back to the Marketing team in weekly team check-ins.
  • Publish several deeply technical blog posts that unpack use cases and best practices for using generative AI for code.
  • Support our Product team with key launch moments.
  • Contribute to the website (sourcegraph.com), and work with the rest of the Marketing team to build out new webpages using your learnings on product use cases.
  • Build out content for, and present in, webinars for our prospects.
  • Deliver best practices resources to the Sales and Customer Engineering teams. For example, this could be a whitepaper outlining where and when Amp is best used for different languages and common development tasks (such as code modernization or unit testing).
  • Establish a formal feedback loop process between Sales, Marketing, and Product teams.
  • Consistently deliver high-quality technical content materials that delight developers and speak to an enterprise audience.
  • Create and maintain a demo flow and talk track for Amp that is commonly used by sellers in the field and marketers at trade shows.
Desired Qualifications
  • 5-7 years of work experience in technical marketing or another customer-facing role, ideally at a company building dev tools.
  • CSS background and you’ve worked in B2B SaaS or a dev tools company, or have experience working on a product for developers.
  • Built technical content or collateral to support a sales team.
  • Excited to work in a startup. Our product is always evolving, and so does our work and our priorities. We stay nimble as we try new things to grow the business.

Sourcegraph is a code intelligence platform that helps developers understand, navigate, and improve large codebases. It provides code search, navigation, bug fixing, refactoring, and performance optimization in one interface, plus an AI assistant named Cody to answer questions and automate tasks by indexing code across repositories. Its strength comes from supporting enterprise-scale code understanding with fast, precise searches over multilingual repositories, strong security and governance, and seamless integration into existing workflows. The goal is to boost engineering velocity, improve software quality, and increase team efficiency by making code easier to explore, reuse, and maintain.

Company Size

51-200

Company Stage

Series D

Total Funding

$223M

Headquarters

San Francisco, California

Founded

2013

Simplify Jobs

Simplify's Take

What believers are saying

  • Palo Alto Networks achieved 30% development velocity gains with Sourcegraph-powered AI integration.
  • Security researchers actively prefer Sourcegraph for bug bounty hunting over GitHub search.
  • Cody 5.1 multi-repo context enables developers to generate entire files with minimal intervention.

What critics are saying

  • September 2025 data breach exposed license keys and customer emails, triggering enterprise churn.
  • SecurifyAI article enables attackers to exploit customer codebases using Sourcegraph's regex capabilities.
  • GitHub Advanced Security and Copilot directly compete, capturing market share from 1M+ users.

What makes Sourcegraph unique

  • Historical commit analysis uncovers deleted secrets and legacy vulnerabilities GitHub search misses.
  • Secure generative AI integration via AWS Bedrock enables enterprise-grade in-IDE assistance.
  • Code Insights analytics platform tracks codebase health metrics for engineering leadership visibility.

Help us improve and share your feedback! Did you find this helpful?

Benefits

Work fully remote

Unlimited PTO

Generous travel budgets

Competitive pay + equity

Medical, dental, & vision

Professional development

Office budget

Wellness budget

Family planning benefits

Growth & Insights and Company News

Headcount

6 month growth

-3%

1 year growth

0%

2 year growth

1%
SecurifyAI
Apr 9th, 2026
Mastering Sourcegraph for Bug Bounty: Advanced Code Dorking Techniques.

Mastering Sourcegraph for Bug Bounty: Advanced Code Dorking Techniques. April 9, 2026 Key Takeaways What security researchers should know immediately. * Sourcegraph outperforms GitHub search for security auditing, especially across large repositories and complex code patterns. * Regex, structural search, and Boolean logic help bug bounty hunters uncover hidden vulnerabilities faster. * Historical commit analysis is a major advantage, making it easier to find deleted secrets and legacy exposures. * Targeted query construction reduces noise, improving signal quality during bug hunting and reconnaissance. * Security teams can shift left more effectively by combining code intelligence with proactive application security workflows. Navigate This Article Table of contents. For security researchers and bug bounty hunters, speed and accuracy are everything. While GitHub is the home of open source, its native search functionality often falls short when you need to dig deep into commit histories or filter through massive repositories for specific vulnerabilities. At SecurifyAI, Securify enable tools that allow Securify to "shift left" and catch bugs faster. In this post, Securify explore why Sourcegraph is a superior alternative to GitHub search for security auditing and how you can use it to uncover hidden risks across large codebases. As part of Mastering Sourcegraph for Bug Bounty: Advanced Code Dorking Techniques, Securify demonstrate how security researchers can leverage powerful regex searches, code intelligence, and large-scale repository analysis to identify vulnerabilities faster and more accurately. The problem with standard GitHub search. If you have ever tried to grep through a massive organization's repository on GitHub, you know the pain: * Speed: GitHub's search often slows down on big repositories. * Depth: It is primarily optimized for basic file and text lookups, often missing complex patterns. * Scope: GitHub usually prohibits cross-repo searches unless you pay for "Advanced Security," and it searches the latest branch rather than the entire history. Why Securify use Sourcegraph for security Dorking. Sourcegraph is a code intelligence platform. It indexes and analyzes code to provide blazing-fast, accurate searches. Here is why it is a game-changer for bug hunting: 1. Regex and Structural Search. Sourcegraph supports Regular Expressions (Regex) and Structural Search. This allows you to search for code patterns rather than just variable names. 2. Boolean operators. You can use AND, OR, and NOT operators for precise filtering. * Example: lang:go auth AND NOT encryption allows you to find authentication code that lacks encryption methods. 3. Historical analysis. Sourcegraph looks through all of the commit histories, not just the current codebase, unlike regular search. This is very important for finding "deleted" credentials or vulnerabilities that are still in history. Tutorial: how to dork for bugs (with demos). Let's look at a real-world workflow using Sourcegraph to find sensitive data. When hunting for secrets, Securify look for specific high-value targets. Common keywords include: * password / pw * AKIA / ASIA (AWS Keys) * clientsecret Step 2: constructing a complex query. Let's say Securify want to find exposed credentials in Python files related to service-now.com, but Securify want to filter out test files to avoid false positives. The Query: service-now.com AND (Passwd OR password OR PW) NOT example NOT test NOT server.service-now lang:python Breakdown of this command: * service-now.com: Restricts results to this specific domain. * AND (Passwd OR...): Targets potential credentials. * NOT example...: Removes dummy data and test servers to ensure the find is legitimate. * lang:python: Limits the search to Python scripts. Step 3: analyzing results. When you run this query, Sourcegraph returns results instantly. In its test case, it returned 87 results in 0.385 seconds. You can immediately see the context. For example, a result might show a requests.post call including auth=(user, pwd), revealing hardcoded "admin" credentials. Comparison: Sourcegraph vs. GitHub search. Conclusion. For developers and security professionals, the ability to "Google" your codebase effectively is a superpower. By moving beyond basic text search and utilizing the structural and regex capabilities of Sourcegraph, you can secure your applications more constructively. To strengthen this process further, teams often combine code search workflows with application security, cloud security, and VAPT services.

Amazon Web Services
Mar 5th, 2026
Palo Alto Networks Accelerates Software Development Using Generative AI with Sourcegraph and Anthropic

Palo Alto Networks accelerates software development using Generative AI with Sourcegraph and Anthropic. 7月 02, 2025 Amazon Web Services. Palo Alto Networks is redefining how software is built in high-stakes industries. In a landscape where outside parties have access to generative AI, speed and precision make all the difference. By harnessing cutting-edge technology from Amazon Web Services (AWS), Palo Alto Networks accelerated its development process and reinforced its commitment to delivering secure, high-quality solutions to its customers. Opportunity | Keeping Pace with Evolving Cyber Threats When you're on the front lines of cybersecurity, standing still isn't an option. Palo Alto Networks, a global leader in cybersecurity, understands that protecting customers against ever-evolving threats is mission critical. To meet this challenge, the company launched an innovative initiative working alongside AWS as well as AWS Partner Anthropic and AWS Partner Sourcegraph to accelerate software development without compromising security. "Bad actors also have access to AI, so we need to stay ahead of them," says Gunjan Patel, director of engineering at Palo Alto Networks. "With AI, they can potentially accelerate their activities, so the good guys need to move faster to keep them in check." Solution | Harnessing Generative AI to Empower Developers Using Amazon Bedrock - the easiest way to build and scale generative AI applications with foundation models - the team securely integrated Anthropic's models into internal workflows. This brought the power of natural language directly into the integrated development environment. Sourcegraph helped weave generative AI capabilities - like debugging, rearchitecting, and writing test cases - into developers' daily routines. This enhanced Palo Alto Networks' productivity without forcing context switching or compromising security. Additionally, it empowered new hires to ramp up faster and helped experienced engineers to focus more on strategic work. "None of the off-the-shelf products were right for us," says Patel. "We needed some sort of customization so that our source code was secured without compromising on any state-of-the-art AI technology. With this solution from AWS, Anthropic, and Sourcegraph, we are bringing access to state-of-the-art cloud models directly into developers' environments." Outcome | Accelerating Development by 30 Percent While Safeguarding Security The impact has been immediate. The company has seen a 30 percent increase in development velocity and has cut onboarding time for new engineers from months to weeks. Today, more than 2,500 developers are already using the generative AI solution, with plans to expand this to 3,500. Developers now work alongside intelligent AI models that act like collaborative teammates, helping them move faster while maintaining the trust and security its customers expect. Harnessing the power of generative AI, Palo Alto Networks modernized its engineering environment. The result is a solution that helps developers enhance their productivity while sustaining the highest security standards. customer-stories generative-ai-trailblazers

VentureBeat
Dec 23rd, 2024
The Code Whisperer: How Anthropic’S Claude Is Changing The Game For Software Developers

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. The software development world is experiencing its biggest transformation since the advent of open-source coding. Artificial intelligence assistants, once viewed with skepticism by professional developers, have become indispensable tools in the $736.96 billion global software development market. One of the products leading this seismic shift is Anthropic’s Claude.Claude is an AI model that has captured the attention of developers worldwide and sparked a fierce battle among tech giants for dominance in AI-powered coding. Claude’s adoption has skyrocketed this year, with the company telling VentureBeat its coding-related revenue surged 1,000% over just the last three months.Software development now accounts for more than 10% of all Claude interactions, making it the model’s most popular use case

DevOps.com
Dec 14th, 2023
Sourcegraph Launches Cody Generative AI Tool to Write Code

Sourcegraph today made generally available an open source Cody tool that leverages generative artificial intelligence (AI) tools to write and fix code.

PR Newswire
Sep 7th, 2023
Introducing Ironclad Contract Ai, A Paradigm Shift For Contract Analysis

Also known as Ironclad CAI, the product allows teams across the organization to generate complex contract analysis and data visualizations using an AI-powered chat interfaceSAN FRANCISCO, Sept. 7, 2023 /PRNewswire/ -- Today, Ironclad , the leading digital contracting platform for modern legal teams, announced the beta launch of Ironclad Contract AI (CAI), a first-of-its-kind product that allows users to complete complex contract analysis across their contract database through an easy-to-use chat interface. The product introduces to the market the use of AI agents to handle complex multi-step analyses by breaking down tasks into subtasks, completing those subtasks, and then evaluating the results to inform the next set of tasks, and is the only legal product that allows users to see the exact steps it took to come to a certain conclusion."Ironclad Contract AI is a quantum leap for contracts," said Ironclad CEO and co-founder Jason Boehmig. "The most legal chatbots have been able to do is return a raw list of results, which still require in-depth analysis by a lawyer. CAI is able to comb through a corpus of contracts and actually do the reasoning required to answer almost any contract-related question—showing you the steps it took to reach that conclusion—and handing that analysis back to your legal team. Where most generative AI is a black box, CAI is an open book."Ironclad CAI helps teams in any organization locate and analyze contracts faster by simply describing the contracts needed or asking questions—without needing filters, advanced keyword logic, or even training

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