Networking Engineer & Cloud Based (Python Development)

The purpose of this role is to support the design, implementation, and evaluation of a measurement\-informed, network\-aware static placement framework (Static NOVA) for geo\-localized multi\-cloud GPU training. The researcher will assist in collecting real\-world cloud network measurements, developing analytical cost and feasibility models, implementing optimization logic, and supporting simulation\-based evaluation for a systems research study.

Master’s degree or PhD (completed or pursuing) in Computer Science Engineering,Information Technology, Networking / Distributed Systems.

Minimum 2\+ years of Experience

Strong academic or applied background in networking research

Python (NumPy, Pandas, Matplotlib)

Networking tools: iperf3, ping, traceroute

Optimization tools or libraries (ILP solvers, OR\-Tools, PuLP)

Cloud platforms (at least one of AWS, GCP, Azure)

Prior experience with systems research, cloud computing, or network performance evaluation

Experience conducting measurement\-based experiments (e.g., benchmarking, performance profiling)

Familiarity with distributed systems concepts (latency, bandwidth, synchronization, throughput)

Experience working on simulation or analytical modeling projects

Prior publication experience (conference or journal) is a plus

• *Strong understanding of:**

Network latency vs bandwidth trade\-offs

Cloud pricing models (compute and network egress pricing)

Distributed machine learning communication patterns

• *Ability to:**

Translate real\-world measurements into analytical models

Design cost and feasibility constraints

Implement static optimization logic

Strong analytical and problem\-solving skills

Ability to document methodology clearly for academic use

The freelance researcher will be responsible for the following tasks

• **Network Measurement**
• Measure inter\-provider bandwidth and latency
• Organize results into structured matrices for analysis
• **Pricing \& Resource Data Collection**
• Collect GPU pricing and network egress pricing from cloud providers
• Document provider\-specific constraints and assumptions
• **Analytical Modelling**
• Assist in modelling distributed training communication traffic
• Develop feasibility constraints based on bandwidth and synchronization limits
• Support compute and network cost modelling
• **Static Placement Framework Implementation**
• Implement a static, network\-aware GPU placement optimizer
• Integrate feasibility constraints into the optimization logic
• **Simulation \& Evaluation**

• *Contact Detail :** 95661 33822

Job Types: Part\-time, Freelance

Contract length: 1 month

Pay: From ₹10,000\.00 per month

Benefits

• Flexible schedule
• Work from home

Work Location: Remote