Endor Labs provides a software supply-chain security platform analyzing open-source and AI-generated code for reachable vulnerabilities, integrating via IDE/CI plugins, and offering fixes, SBOMs, artifact signing, and pipeline monitoring.
<h2><strong>About Us</strong></h2> <p>Endor Labs is building the Application Security platform for the software development revolution. Modern software is complex and dependency-rich, making it increasingly difficult to pinpoint the risks that truly matter. Endor Labs solves this challenge by building a call graph of your entire software estate—enabling teams to clearly identify, prioritize, and fix critical risks faster. </p> <p>Trusted by companies that are one or one hundred years old, Endor Labs secures code whether it was written by humans or AI, and whether it's 40-year old C++ code or cutting edge Bazel Monorepos. Endor Labs was founded by serial entrepreneurs Varun Badhwar and Dimitri Stiliadis, and is backed by leading VC firms such as Dell Technology Capital, Lightspeed, and Sierra Ventures.</p> <p>Sound interesting? Let’s talk if you want to be part of the next big leap in security innovation!</p> <h3><strong>About the Role</strong></h3> <p>We are looking for a Program Analysis Engineer with an interest in program analysis, static analysis, or machine learning for code, to join our engineering organization. This is an individual contributor role focused on our static analysis and ML-for-code initiatives.</p> <p>As an MTS, you will contribute to our existing suite of static analysis tools, help evolve our SCA (Software Composition Analysis) and SAST (Static Application Security Testing) platforms, and help shape machine-learning experiments that power the next generation of our detection and prioritization capabilities. You will partner closely with engineering, product, and research peers to translate customer needs into production systems that run at scale.</p> <p>This role is a good fit for engineers who enjoy working on programming languages and code analysis, who are curious about applying ML techniques to real-world code and AppSec problems, and who thrive in a fast-paced startup environment.</p> <p><em>This role is based in Palo Alto, California, and follows a hybrid work model (3 days p/week onsite).</em></p> <h2><strong>How You'll Make an Impact</strong></h2> <ul> <li>Contribute to our existing suite of static analysis tools, helping improve precision, recall, coverage, and performance across supported languages.</li> <li>Help evolve our SCA and SAST platforms, shaping how we detect vulnerabilities, analyze dependencies, and prioritize risk across the software supply chain.</li> <li>Participate in machine-learning experiments for SCA and SAST, from problem framing and dataset work through evaluation and the path from prototype to production.</li> <li>Contribute to our program-analysis infrastructure, which includes call graph construction, data-flow and taint analysis, and language-specific analyzers.</li> <li>Work with product and engineering to help shape parts of the technical roadmap for analysis and ML-for-code work.</li> <li>Collaborate with engineers across the team through design discussions, code reviews, and hands-on pairing.</li> <li>Opportunities to represent Endor Labs externally—e.g., talks at technical conferences, meetups, or industry events—if that's something you enjoy.</li> <li>Engage with customers when needed—triaging findings, explaining analysis behavior, and turning field feedback into product improvements.</li> <li>Help identify and address performance bottlenecks, reliability issues, and technical debt across the analysis stack.</li> </ul> <h2><strong>What You Bring to the Table</strong></h2> <ul> <li>Experience with <strong>program analysis or static analysis</strong> (e.g., call graphs, data-flow or taint analysis, abstract interpretation, symbolic execution) <strong>or</strong> to <strong>ML for code</strong> (e.g., vulnerability detection, code representation learning, program repair, code embeddings, reachability prediction, LLM-based code understanding). Deep expertise in one area is great; a working familiarity and eagerness to learn is also welcome.</li> <li>A software engineering background—industry experience, research, open source, or a mix.</li> <li>Comfort working in at least one of Java, Python, or Go, and the ability to ship production-quality code (or the trajectory to get there quickly).</li> <li>A product mindset—you care about building things that work well for users, not just technically interesting code.</li> <li>Good communication and collaboration skills. You can work through technical trade-offs with teammates and explain your thinking clearly.</li> </ul> <h3><strong>Bonus Points</strong></h3> <ul> <li>Interest in giving talks at technical conferences, workshops, or industry events, or in engaging directly with customers. Neither is required.</li> <li>Familiarity with AppSec, DevSecOps, or software supply chain security.</li> <li>Experience with ML systems in production (training pipelines, evaluation frameworks, model serving, feedback loops).</li> <li>Contributions to open-source static analysis tools, program analysis frameworks, or security research.</li> <li>Experience at an early-stage startup or as a founding engineer.</li> <li>Experience building developer-facing products or platforms.</li> </ul> <h2><strong>Why Endor Labs</strong></h2> <p>We’re building at the intersection of developer productivity and security — one of the fastest-growing spaces in software. Our dev-loved platform has real ROI, strong momentum, and customers who care about doing things right.</p> <p>At Endor Labs, we think big, start small, and learn fast. We take ownership, move with purpose, and always start with the customer’s success. We debate with data, make the complex simple, and challenge each other with kindness and candor. We celebrate wins, learn from misses, and have fun along the way — because when our customers win, we all win.</p> <p><strong>Endor Labs is an Equal Opportunity Employer.</strong> We celebrate diversity and are committed to creating an inclusive environment for all employees. Even if you don't fit every