.png){kind=logo}
The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.
About RunSybil
RunSybil is an AI security startup scaling offensive security services by automating hacker intuition. We’re building Sybil: an AI-powered penetration testing product that finds vulnerabilities faster than human hackers. Backed by strong investor support and early customer traction, our team is composed of experts from the likes of OpenAI, Meta, Mandiant, Palantir, Cruise, and Trail of Bits.
About this Role
We are seeking talented engineers intent on changing the security industry. If you have experience on fast-moving teams and pushing out high quality experiments: we want to talk to you.
What You’ll Do:
- Propose and implement technical initiatives based on product roadmap needs
- Lead technical design discussions, contribute to team decisions, and provide feedback on engineering approaches
- Translate ambiguous technical challenges into actionable plans, and execute them in collaboration with the team
- Serve as the cornerstone of our engineering culture by setting and upholding exemplary standards for code quality, practices, and craftsmanship
We’re Looking For Someone Who Has A Mix of The Following:
- Experience working with security data problems
- MS or PhD in a quantitative discipline is nice but certainly not required - strong bias for building product-informing experiments versus published works
- 4+ years of experience designing experiments and managing data infrastructure
- Understanding of both modern and classic machine learning techniques
- Equally comfortable with Jupyter notebooks and building data pipelines
- Seeks autonomy, creative problem-solving, and moving quickly in an ambiguous environment
Location:
Hybrid role based in New York City. Some travel may be required.
What We Offer:
- Competitive Salary: Attractive startup compensation package with equity options
- Benefits: Excellent medical, dental, and vision plans. Unlimited PTO
- Growth Opportunities and Early Impact: Work directly with and learn from industry leaders while driving significant impact in our fast-growing security startup
We understand not every applicant will meet every qualification. We value hard work and potential, and still want to hear from you!
Similar Jobs
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Research, prototype, build, and deploy predictive products using statistical analysis and machine learning. Analyze large financial datasets, formulate hypotheses, develop and evaluate models, productionize algorithms, write reliability tests, conduct peer reviews, and maintain expertise in machine learning, data analysis, MLOps, and agentic LLM systems.
Top Skills:
Agentic SystemsArtificial IntelligenceJupyterLarge Language ModelsMachine LearningMachine Learning OperationsMatplotlibNatural Language ProcessingPandasScikit-Learn
Fintech • Machine Learning • Payments • Software • Financial Services
Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.
Top Skills:
AWSAzureC++Ci/CdGCPGoJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorFlow
Fintech • Machine Learning • Payments • Software • Financial Services
Build, deploy, scale, and monitor machine learning models and production services. Responsibilities include model development, feature selection, data pipeline construction, distributed data processing, cloud and Kubernetes operations, CI/CD automation, testing, model governance, responsible AI, and collaboration with product and data science teams.
Top Skills:
SparkAWSAzureC++Ci/CdGCPGoJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnTensorFlow
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
