Machine Learning Engineer (Remote)

We're looking for a Machine Learning Engineer who loves building real products and shipping code. If you enjoy owning production systems, solving tough engineering problems, and bringing cool ML research into real-world applications, you’ll love it here!

Why Us:

Identity security today sucks. People hate passwords, 2FA codes, and security questions; it's an endless cycle of frustration. At TWOSENSE.AI, we're fixing this using AI-powered behavioral biometrics. The system we created automatically recognizes people by their unique behaviors—how you type, move the mouse, or even walk—creating the world’s first invisible, private biometric. No passwords, no puzzles—just seamless security that's always on. Our mission is to fundamentally change secure human-computer interactions, making forgotten passwords and frustrating authentication a thing of the past.

We're an engineer-founded and led team of PhDs and exceptional software engineers based in Brooklyn, NYC. Transparency, autonomy, continuous improvement, and strong engineering culture matter deeply to us. Right now, we're fully remote and plan to stay flexible for the foreseeable future. As an early team member, you'll directly shape our strategy, trajectory, and your own career as you grow with us.

What You'll Do:

• Build and maintain our production ML pipeline—including ETL processes, data cleaning, preprocessing, feature extraction, training, evaluation, deployment, and monitoring.
• Develop streamlined ML workflows to effectively support our production systems.
• Write clean, maintainable Python code using test-driven or test-first development practices.
• Collaborate closely with founders and researchers to bring ML ideas to life—with opportunities to participate directly in research projects.
• Optimize our infrastructure to handle growth and scale effectively.

Must-Have Qualifications:

• Strong software engineering skills—grounded in SOLID principles and best practices.
• Hands-on experience deploying ML models to production.
• Experience with common ML libraries like scikit-learn, TensorFlow, or PyTorch.
• Basic understanding of ML fundamentals (algorithms, math/stats), along with strong intuition for how and when to apply different modeling approaches.

Nice-to-Have Qualifications:

• Familiarity with developing and deploying ML systems using AWS tools and infrastructure.
• Experience with varied data types (structured, time-series).
• Previous experience with behavioral biometrics or security-focused products.
• Previous experience with ONNX.