Design, build, and evolve scalable full stack product features and customer-facing cloud applications.
Locations (2)
Bangalore, Karnataka, India
India
Responsibilities
Design, build, and evolve scalable full stack product features and customer-facing cloud applications.
Own complex product capabilities end-to-end, from detailed technical design through frontend and backend implementation to production readiness.
Remain deeply hands-on, writing high-quality code and solving complex technical problems across the stack.
Partner with Architects, Product and UX to translate customer needs and product direction into production software, making pragmatic technical decisions and identifying trade-offs.
Improve the reliability, performance, security, observability, testability and maintainability of our applications and services.
Lead architecture, development, and delivery of open‑source, high‑performance network security and AI reference solutions for the Network Software Enabling team in the Enterprise/Cloud Networking segment.
Compensation
Annual Salary Range for this job which could be performed in Ireland is: 76,000.00 € - €142,100.00
Responsibilities
Own the architecture and technical roadmap for one or more network security reference solutions
Design and optimize packet‑processing and security pipelines for throughput, latency, and TCO on current and upcoming Intel® Xeon® platforms
Integrate hardware acceleration technologies into open‑source security stacks such as Snort3, VPP, and Hyperscan
Enable and optimize AI/ML models for security use cases
Lead performance characterization, define benchmarking methods, and produce collateral that shows customer value
Requirements
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline
Nice to have: Master's or PhD in Computer Science, Electrical Engineering, or a related field
Nice to have: network security: firewall, IPS/IDS, IPsec/TLS, VPN, deep packet inspection (DPI)
Nice to have: AI/ML training and inference optimization
Lead gameplay programming for AI systems in Cyberpunk 2, designing and implementing low-level systems for open world AI and procedural content.
Compensation
Estimated pay range for this position: $180,100 - $247,600 CAD annually. This role may be eligible for a long term incentive program (option to purchase CDPR stock after the vesting period).
Responsibilities
Work closely with designers and other developers to understand their needs and ensure efficient world population
Build and maintain major systems for open world spawning, AI, and systemic gameplay
Monitor performance and memory usage, optimizing as needed
Oversee technical efforts of other developers through code reviews and mentoring
Develop debug and designer tools for non-deterministic gameplay
Building and maintaining high-performance infrastructure for large-scale LLM training, reinforcement learning, and inference, including distributed runtimes and GPU cluster scheduling.
Locations (2)
New York City, New York, United States
Paris, Île-de-France, France
Responsibilities
Design, build, and operate distributed training and inference infrastructure for large language models, including Mixture-of-Experts models
Scale training with parallelism strategies (data, tensor, pipeline, and expert), memory optimization, checkpointing, and failure recovery
Build inference and rollout systems that support evaluation, experimentation, and RL without slowing down training
Build execution environments and sandboxes for online RL and agent post-training
Own continuous performance validation: regular benchmarks of rollout speed, training throughput, and learning behavior
Lead LLM inference optimization for Qualcomm Cloud AI accelerators, working with customers and internal teams to deploy and tune generative AI workloads.
Responsibilities
Deploy and optimize LLM and multimodal inference workloads on Qualcomm Cloud AI accelerator platforms
Profile and optimize prefill and decode performance metrics
Analyze model architecture and runtime bottlenecks across compute, accelerator memory, host memory, KV cache, interconnect, networking, and software stack
Optimize model execution using quantization, batching, continuous/dynamic batching, KV-cache management, paged attention, prompt/context-length tuning, and speculative decoding
Optimize distributed inference across tensor, pipeline, data, context, and expert parallelism
Requirements
Master’s degree or higher in Computer Science, Electrical/Computer Engineering, AI/ML, or related technical field
Company-paid medical healthcare (dental, vision, and mental).
Free mental health support, including access to counseling, psychiatric care, and a variety of well-being webinars.
Paid leave — 26 days of vacation, 10 sick days, & 12 calendar holidays per year.
RRSP with employer matching.
Lifestyle Spending Account (LSA) – $100 per month to use towards fitness, wellness, internet, home office equipment, learning, streaming services, and more.
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