gsnoff

• Optimized hot code paths in behavioral prediction modules, achieving up to 60% decrease (~2000 to 800 ns on local i5-11 machine) in simulation step times; • Optimized memory throughput by implementing RCU-based lock-free caching, resulting in a measurable reduction in CPU contention of up to 50% in certain scenarios; • Optimized CI pipeline jobs by introducing and troubleshooting Github Actions caches populated with compiler-generated artifacts, reducing average build job time from 20 to 4 minutes; • Reduced state sync delays by ~3 times in average by implementing and plugging in custom EVM glue for on-the-node batch processing; • Introduced AI-assisted debugging (via Devin) and performance analysis, improving engineering leading to the team’s increase of debugging and optimization task efficiency indicators by at least 30%; • Prevented a whole class of regressions by introducing a Zstd-compressed time-series fixture format in Git LFS, enabling mandatory CI runs of integration tests which previously depended on archive node availability; • Balanced throughput and latency by fine-tuning the scheduling of CPU-bound (Rayon) and I/O-bound (Tokio) task OS threads side by side, increasing success rate of multihop swap opportunities by 5–10%; - Drove architectural discussions focused on scalability, maintainability, and technical debt reduction, mentoring 2 developers on project architecture and general low level practices.

• Eliminated recurring production outages by identifying and resolving a complex cross-language Rust/C memory corruption issue, saving approximately ~20 engineering hours spent on 30+ intermittent production downtime incidents per week; • Reduced mass-deployment hardware costs by up to 60% by reverse-engineering proprietary access point boot processes, enabling large-scale deployment of the lower-cost networking devices; • Developed and deployed a custom OpenWRT-based firmware platform enabling centralized, billable Wi-Fi services across 400+ commercial/retail locations with no single point of failure; • Implemented a zero-downtime A/B firmware update system, enabling seamless over-the-air upgrades for 2500+ devices, ensuring uninterrupted service for end-users; • Enabled centralized monitoring and remote management of near 95% of device functionality by building a comprehensive backend API layer for the customer dashboard.

• Engineered ultra-low-latency execution and market data adapters for multiple centralized exchanges using WebSocket, FIX 4.4, and REST APIs, optimizing throughput to handle 5–10K+ events/sec; • Reduced mean time to resolution (MTTR) for performance regressions by up to 70% by architecting a comprehensive distributed tracing framework using OpenTelemetry across asynchronous services; • Reduced p99 latency by 20–30% across latency-critical trading workflows by identifying CPU bottlenecks and eliminating lock contention through performance profiling.

• Engineered high-throughput networking packet processing pipelines, sustaining 10+ Gbps line-rate traffic, using Rust, modern C++, DPDK, and zero-copy architectures; • Identified and reduced CPU cache invalidations and pipeline stalls through rigorous profiling and memory layout optimization, achieving up to 15–20% performance gains on certain production workloads; • Fixed optimization-breaking UB from C-style type punning by backporting a standard-compliant std::bit_cast polyfill for GCC 10 that respects C++ strict aliasing and object lifetime rules; • Improved efficiency of matcher primitives by up to 30% by facilitating copy elision and replacing heap collection references with view types for implicit argument conversions; • Reduced networking layer code duplication by 6–8% and improved maintainability by modernizing legacy components with reusable, type-safe C++20 templates and concepts.

• Built a highly parallelized counterfactual regret minimization (CFR) engine from the ground-up, delivering a core distributed backend for large-scale game theory simulations under heavy confidentiality constraints; • Orchestrated cloud infrastructure for complex ML training workflows, utilizing Amazon APIs and automation tools to dynamically spin up and configure optimized cloud instances; • Reduced AWS EC2 compute overhead by ~30% through deep profiling and elimination of cross-node NUMA bottlenecks in a highly concurrent solver training workload; • Streamlined historical data reconciliation by implementing an automated batch ELT workflow with ClickHouse as persistence backend, reducing snapshot processing latency by 98–99%.

• Implemented a dynamic pricing and automatic client suspension module for cEnergo smart metering system, orchestrating its operation with 8 existing business logic modules (metering device accounting, measurement streaming, state synchronization etc.), each with its own model-level interface; • Ported an MCU-based bare metal firmware codebase to a Linux-based platform in under a month; • Reverse engineered a proprietary smart card based billing protocol by capturing USB packets exchanged by a rebranded in-browser ActiveX control with specific non-PC/SC (T=14) card reader models; • Implemented stable remote firmware update reflashing onto raw MTD partitions with thorough integrity checks; • Implemented a comprehensive REST-based API endpoint in smart metering system for foreign clients.