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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Recruiting from Scratch - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $250,000.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Algorithmic Trading, Databases, Data Infrastructure, Distributed Systems, Fraud Prevention and Detection, Internet Security, Intrusion Detection and Prevention, Python (Programming Language), Machine Learning, Natural Language Processing, Node.Js, Performance Tuning, Software Engineering, Data Streaming, TypeScript, Feature Store, Data Processing, Feature Engineering, ReactJS, Model Validation, Backend, Production Code, Performance Monitor, Machine Learning Operations, Data Pipelines - **Published:** November 8, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-san-francisco-ca--95227395-0a4b-496c-9fe0-88129e0ab562 ## About the Role Machine Learning, Machine Learning, Internet Security, Artificial Intelligence (AI), Analysis Skills, Performance Modeling, Customer Relations, Technical Leadership, Security Attacks, Natural Language Processing (NLP), * 3-8 years of experience in machine learning engineering, with at least 4 years preferred based on the role requirements. * Strong experience building, training, and deploying machine learning models in production using Python. * Experience working with real-time or streaming data pipelines and large-scale production systems. * Strong quantitative fundamentals, including probability, statistics, and linear algebra. * Experience with anomaly detection, behavioral modeling, NLP, or other relevant machine learning techniques. * Strong software engineering fundamentals and the ability to build clean, maintainable production code. * Experience owning machine learning systems end to end, beyond experimentation or notebook-based modeling. * Understanding of feature stores, high-performance databases, and distributed systems. * Ability to evaluate different modeling approaches and understand their strengths, limitations, and trade-offs. * Comfortable working in a small, fast-moving engineering team with significant autonomy. * Strong ownership mentality and ability to take a problem from identification through implementation and production rollout. Technical Skills Machine Learning * Model training and deployment. * Anomaly detection. * Behavioral modeling. * Probability and statistics. * Linear algebra. * Natural Language Processing (NLP). * Model selection and performance optimization. Production ML Engineering * Python. * Real-time and streaming data pipelines. * Feature stores and feature engineering. * High-performance databases. * Distributed systems. * Production model monitoring and evaluation. * Scalable ML infrastructure. Software Engineering * Production-grade coding practices. * System design and architecture. * Performance optimization. * Scalable backend systems. * End-to-end technical ownership. Preferred Background * Experience in cybersecurity, fraud detection, account takeover prevention, or threat detection. * Experience working with high-volume behavioral or event data. * Background in quantitative finance, trading systems, or other high-stakes data environments. * Experience building real-time anomaly detection or risk-scoring systems. * Experience designing feature pipelines and high-performance ML infrastructure. * Experience working in an early-stage startup or lean engineering team. * Familiarity with Node.js, TypeScript, or React is helpful but not a core requirement. Working Style * High ownership: Comfortable owning important systems and making decisions independently. * Strong execution: Able to identify problems, propose solutions, and deliver without needing constant direction. * Customer-oriented: Interested in understanding real customer problems and translating them into better detection capabilities. * Practical engineering judgment: Knows how to balance model performance, scalability, reliability, and speed of execution. * Fast learner: Can quickly understand unfamiliar problems and technical domains. * High standards: Takes pride in building reliable, maintainable systems. * Collaborative: Comfortable working closely with the CTO, ML leadership, and a small engineering team., * Visa: Must be authorized to work in the US without requiring current or future sponsorship. ## Description * Design, build, and evolve machine learning models that identify account compromise and suspicious behavior in real time. * Work closely with technical leadership to develop the core detection platform and improve its ability to identify emerging attack patterns. * Research, select, train, and deploy models based on the specific detection problem and available data. * Explore new signals and modeling approaches that improve detection coverage while minimizing false positives. * Work with behavioral modeling, anomaly detection, statistical methods, and NLP to identify sophisticated security threats. Production ML Engineering * Take machine learning models from experimentation and research through production deployment. * Monitor model performance in live environments and continuously improve accuracy, scalability, and reliability. * Build and optimize real-time data pipelines, feature infrastructure, and systems that support production model inference. * Design systems that can handle significant increases in data volume and customer usage. * Make thoughtful architectural decisions around performance, scalability, and high-throughput data processing. Technical Ownership & Collaboration * Own meaningful parts of the machine learning detection platform end to end. * Work directly with customers to understand the attacks they face and identify opportunities for better detection. * Collaborate with the CTO and machine learning leadership on technical direction and modeling decisions. * Establish engineering standards, contribute to architectural decisions, and raise the overall quality of the platform. * Take initiative in identifying problems, proposing solutions, and executing improvements across the system., * Work on real-world security problems: Develop models that identify active threats and protect businesses against account compromise. * Move beyond research: Take ownership of models throughout their lifecycle, from experimentation to production deployment and live performance monitoring. * Have significant technical influence: Work closely with the CTO and ML leadership in a small, high-caliber team. * Build for scale: Help architect a platform designed to handle significant growth in customers, data, and detection workloads. * Work close to customers: Understand emerging attacks and use real-world feedback to improve detection capabilities.