About the job :

We're looking for a motivated, highly talented Engineer to join our AI engineering team. You'll work with world-class engineers to design and enhance the AI models that power our security products. Learn how to build and scale the underlying system that hosts these models. If you love solving hard problems and experimenting with the NLP model architectures and the latest LLMs and are eager to dive deep into the internals of the underlying systems and ultimately make a real impact, this is your chance to jump in early and grow fast.

Responsibilities :

- Build and ship the AI systems that power Nightfall's security products.

- Work hands-on with Python to analyze data, experiment with new natural language or other model architectures, benchmark and monitor model accuracy, build automation for automated training and deployment, and do real-time inference.

- Work hands-on with Go to build the microservices that ingest and process data at scale through the use of streaming infrastructure, distributed caching, and highly concurrent execution.

- Collaborate across engineering and product to turn ideas into live AI features.

- Participate in the team's on-call rotation to help drive operational excellence.

Requirements :

- 6+ years of industry experience with a graduate degree in computer science or engineering with rigorous coursework in artificial intelligence, machine learning, or a related field.

- Code fluently in Python and Go (or C++ or Java) with a solid understanding of data structures and algorithms.

- Hands-on experience with building deep learning models.

- Thrive in fast-moving, high-ownership environments.

- Are excited to learn from experienced mentors and level up quickly.

Bonus Points For :

- Hands-on experience with using PyTorch, spaCy, Transformer, CNN, LSTM, or other deep learning architectures to solve complex problems in NLP or other domains

- Familiarity with AWS and GCP, and experience with systems such as Kafka, Redis, Postgres, etc.

- Hands-on experience with MLOps tools for training, inferencing, evaluation, monitoring, and deployment.

- Hackathon wins, research papers, or open-source contributions.