Machine Learning Engineer

Forward Networks is revolutionizing the way large networks are managed. The Forward Enterprise platform delivers a vendor-agnostic "digital twin" of the network, based on a mathematical model. The platform scales to support hundreds of thousands of network devices, whether cloud, hybrid cloud, or on-prem. It serves as a single source of truth for the network, enabling network operators to instantly verify security posture, accelerate troubleshooting, avoid outages, and modernize network management.

Over the past few years, Forward Networks has received tremendous industry recognition, including “Cool Vendor in Enterprise Networking” by Gartner, “Product of the Year” by Cloud Computing, “Enterprise Cloud Computing Software of the Year,” and has been named to Fortune’s 2022 “Best Workplaces in the Bay Area” list.

The company was founded by four Stanford PhD graduates who saw a massive opportunity to improve network operations. Investors include Andreessen Horowitz, Threshold Ventures, and Goldman Sachs.

Forward Networks is looking for an applied Machine Learning (ML) Engineer to help create artificial intelligence products. You will be working closely with domain experts and the leadership team to help guide and implement Forward's AI strategy.

Responsibilities:

• Design machine learning systems.

• Research and implement appropriate ML algorithms, applications and tools.

• Select appropriate datasets and data representation methods.

• Run machine learning tests and experiments.

• Perform statistical analysis and fine-tuning using test results.

• Train and retrain systems when necessary.

• Extend existing ML libraries and frameworks, if necessary.

• Keep abreast of latest developments in the field.

Requirements and skills:

• Proven experience as a Machine Learning Engineer or similar role.

• Deep knowledge of the latest trends in Generative AI -- LLMs, libraries (e.g. LangChain), tools and techniques.

• Understanding of data structures, data modeling and software architecture.

• Deep knowledge of math, probability, statistics and algorithms.

• Ability to write robust code in Python and/or Java.

• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).

• Excellent communication skills.

• Ability to collaborate with the non-AI-experts and come up with innovative domain-specific applications.

• Outstanding analytical and problem-solving skills.

• BS in Computer Science, Mathematics or similar field; Master’s degree is a plus.