Machine Learning Engineer
We are looking to expand our team to include a full-time Machine Learning Engineer.
Identity security is completely failing to meet our needs. The current paradigm creates a puzzle that only the authorized user can solve. Humans are constantly solving these puzzles (passwords, text messages, emails, secret questions, dongles, etc), which is a universally terrible experience, and as soon as we complete these tasks, that information is out of date. TWOSENSE.AI is software that automates all the work the user has to do, so security is invisible and always on at every second. We use AI to enable any system to learn to recognize the user based on behavior, such as gait, typing behavior, mouse movement, usage patterns, and many more, creating the world’s first invisible, private biometric. Our mission is to fundamentally change the nature of secure human-computer interaction. We’re creating a future where how bad it is today to forget a password, or to type in 6-digit codes from text messages, will be a fading memory.
This is a unique opportunity to join an ML-driven team just after liftoff. We’re an engineer-founded and led team of PhDs and outstanding Software Engineers out of Brooklyn, NYC. We value transparency, agency, continuous improvement, and engineering culture. We are currently fully remote, but with the intention of returning to an office setting at some point in the future.
What you will do:
You'll be working directly with the founding team on our core product. Your day-to-day will be a mix of data science (classical ML work) and applying new deep learning techniques from image, video, and voice processing research to behavioral data. This ML role is focused on our research infrastructure and production ML infrastructure including everything from ETL, data cleaning, preprocessing, feature extraction, pipelining, productizing, deployment, and monitoring. The majority of the work is focused on writing production-grade ML code and infrastructure, with opportunities to participate in research activities as well.
What we’re looking for:
- Strong Software Engineering fundamentals
- Strong experience with ML code in production settings (e.g., collaboration and code review, version control, monitoring, and writing tests)
- Strong Python skills
- Strong experience with ML tools (eg. scikit-learn)
- Experience with deep learning frameworks (eg. TensorFlow)
- Experience with MLOps tools including ML pipelines and workflow orchestration platforms (eg. Kubeflow) and experiment tracking (eg. MLflow)
- Experience with ML basics and fundamentals (algorithms, math, statistics)
- Experience with varied data types