Applied Scientist, Network Fabric Engineering

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
We are looking for an Applied Scientist to join our team and tackle some of the most challenging traffic engineering problems at planetary scale. You will develop novel optimization algorithms
and ML models that balance network utilization, resilience, cost efficiency, and latency — across traffic classes with fundamentally different characteristics. The problem then expands to network capacity planning, where you will collaborate with other scientists to develop the optimal strategy to efficiently scale the network.

Key job responsibilities
- Formulate traffic engineering problems as mathematical optimization models and develop scalable solvers that operate on graphs with hundreds of nodes and O(10^4) edges
- Design ML models for traffic demand prediction, anomaly detection, and workload classification
- Develop capacity planning frameworks that co-optimize cost, reliability, and performance over multi-year horizons
- Build simulation and evaluation frameworks to validate TE algorithms against realistic failure scenarios and traffic patterns
- Publish research at top venues (SIGCOMM, NSDI, INFOCOM, NeurIPS, ICML) and contribute to the scientific community
- Collaborate with network engineers, software engineers, and operations teams to bring algorithms from prototype to production at global scale
- Define metrics, run A/B experiments, and measure the real-world impact of algorithmic changes on network performance

About the team
The Inter Data Center (InterDC) Networking team owns traffic engineering across AWS's regions — the largest inter-datacenter network in the world, connecting hundreds of data centers
across dozens of regions. We design and implement the algorithms, optimization systems, and scientific frameworks that decide how exabytes of traffic traverse this network every day. Our work
sits at the intersection of combinatorial optimization, machine learning, distributed systems, and network engineering.