Were looking for an experienced Network Researcher to join our team. Youll investigate how modern networks behave at scalediving deep into protocols, traffic patterns, and path performanceto design data-driven improvements that enhance reliability, efficiency, and user experience across our companys global network. Youll develop analysis tooling, run large-scale measurements on top of our big-data platform, and share findings internally and externally.
Responsibilities:
Build tools & pipelines: Develop analysis tools, datasets, and reproducible pipelines to support research at scale.
Deep protocol analysis: Perform in-depth studies of protocol behavior (TCP congestion control, QUIC/HTTP/3, HTTP/2, DNS, DHCP) including edge cases, timeouts, retransmissions, handshake dynamics, and head-of-line effects.
Network measurement & modeling: Analyze latency, jitter, loss, throughput, and path selection. Build models and KPIs that explain and predict performance.
Experimentation: Design controlled experiments and A/B tests; reproduce findings in lab environments (emulation/simulation) and validate on real traffic.
Collaboration with engineering: Translate research into product improvements and platform capabilities; deliver clear specs and reference implementations.
Communication & thought leadership: Publish results (internal reports, blog posts, talks), create visualizations, andwhen relevantcontribute to community.
Responsibilities:
Build tools & pipelines: Develop analysis tools, datasets, and reproducible pipelines to support research at scale.
Deep protocol analysis: Perform in-depth studies of protocol behavior (TCP congestion control, QUIC/HTTP/3, HTTP/2, DNS, DHCP) including edge cases, timeouts, retransmissions, handshake dynamics, and head-of-line effects.
Network measurement & modeling: Analyze latency, jitter, loss, throughput, and path selection. Build models and KPIs that explain and predict performance.
Experimentation: Design controlled experiments and A/B tests; reproduce findings in lab environments (emulation/simulation) and validate on real traffic.
Collaboration with engineering: Translate research into product improvements and platform capabilities; deliver clear specs and reference implementations.
Communication & thought leadership: Publish results (internal reports, blog posts, talks), create visualizations, andwhen relevantcontribute to community.
Requirements:
Education: BSc + MSc in Computer Science
Networking expertise (must): Strong foundations in computer networking and Internet protocols: TCP/IP, QUIC/HTTP/3, HTTP/2, DNS, RDP, DHCP, routing (BGP/OSPF/IS-IS), MPLS, NAT, load balancing.
Data & coding (must): Proficiency in at least one programming language (e.g., Python, Go, or Java) and solid SQL skills; comfort working with large datasets and time-series analysis.
Big-data & analytics: Experience with big-data platforms and frameworks (e.g., Spark) and databases (NoSQL/relational such as Elasticsearch, MongoDB, MySQL, AWS Athena).
AI/ML for network analytics: Practical experience applying machine learning to packet/flow/time-series datafeature engineering, clustering/segmentation, anomaly detection, forecasting, and causal analysisto model performance and inform routing/QoS optimizations; proficiency with Python data stack (pandas, NumPy, scikit-learn); familiarity with PyTorch or TensorFlow is a plus.
Tools: Hands-on experience with Wireshark, tcpdump/tshark, Scapy, iperf; ability to craft custom parsers and automate experiments.
Excellent English and communication skills
Team player, responsible, and well-organized.
Education: BSc + MSc in Computer Science
Networking expertise (must): Strong foundations in computer networking and Internet protocols: TCP/IP, QUIC/HTTP/3, HTTP/2, DNS, RDP, DHCP, routing (BGP/OSPF/IS-IS), MPLS, NAT, load balancing.
Data & coding (must): Proficiency in at least one programming language (e.g., Python, Go, or Java) and solid SQL skills; comfort working with large datasets and time-series analysis.
Big-data & analytics: Experience with big-data platforms and frameworks (e.g., Spark) and databases (NoSQL/relational such as Elasticsearch, MongoDB, MySQL, AWS Athena).
AI/ML for network analytics: Practical experience applying machine learning to packet/flow/time-series datafeature engineering, clustering/segmentation, anomaly detection, forecasting, and causal analysisto model performance and inform routing/QoS optimizations; proficiency with Python data stack (pandas, NumPy, scikit-learn); familiarity with PyTorch or TensorFlow is a plus.
Tools: Hands-on experience with Wireshark, tcpdump/tshark, Scapy, iperf; ability to craft custom parsers and automate experiments.
Excellent English and communication skills
Team player, responsible, and well-organized.
This position is open to all candidates.
