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# Principal Software Engineer

- **Company:** Circana, LLC.
- **Location:** Chicago, IL, United States (Remote available)
- **Salary:** $250,000.0
- **Contract:** Permanent contract
- **Skills:** Aliasing, Amazon S3, Systems Engineering, Big Data, C++ (Programming Language), Cloud Engineering, Code Review, Encodings, Nvidia CUDA, Computer Programming, Databases, Computer Engineering, Serialization, Data Structures, Software Debugging, Distributed Data Store, MapReduce, Search Algorithms, Message Passing Interface, OpenMP, Open Source Technology, Performance Tuning, Remote Direct Memory Access, Memory Leaks, High Performance Computing, Concurrency, Distributed Programming, HNSW (Hierarchical Navigable Small World), Parallel Computation, Backend, Discretization, Low Latency, Performance Monitor, Search Engines, ROCm, Machine Learning Operations, Vertica, C++14
- **Published:** October 12, 2026
- **Apply:** https://startup.jobs/principal-software-engineer-c-distributed-systems-hybrid-chicago-il-circana-com-10397534

## About the Role

* Modern C++ Expertise: 12+ years of production experience writing hands-on, low-level systems in C++ (C++17/20/23 preferred). Mastery of template metaprogramming, concepts, coroutines, and custom memory allocators is key.
 * HPC Parallelism: Very good to expert-level knowledge of cluster-scale distributed programming using MPI (Message Passing Interface) paired with shared-memory thread-level programming via OpenMP and MapReduce frameworks.
 * Hardware-Aware Engineering: Documented experience with microarchitectural optimization, loop vectorization, pointer-aliasing constraints, and manual SIMD intrinsics programming.
 * Concurrency Mechanics: Deep understanding of OS internals, memory barriers, lock-free data structures, and atomics (std::atomic).
 * Database Internals: Practical experience designing core database subsystems (e.g., vectorized query operators, LSM-trees, B-trees, custom buffer pools, columnar serialization frameworks and query plan optimizers).
 * Strong debugging, performance tuning, and problem-solving skills.
 * Proficiency in one or more leading memory and compute monitoring/performance tools (such as Valgrind, Purify, Intel/AMD/ARM diagnostics etc.), * Track record contributing to open-source or work experience on proprietary distributed engines (e.g., ClickHouse, DuckDB, Doris, CockroachDB, RocksDB, ScyllaDB, or Velox).
 * Experience with cloud-native distributed, elastic environments, object storage tiering (S3/GCS), or zero-copy networking.
 * Experience integrating heterogeneous acceleration (NVIDIA CUDA / ROCm) into big data workloads and hybrid CPU/GPU setups.
 * Vector & Graph Algorithmic experience: Experience building or modifying high-performance vector index/search engines, graph-based indices, or localized quantization frameworks.
 * Functional domain awareness - CPG, Retail, Pharma verticals

## Description

* Architecture & Design: Play a key role in core architectural choices and roadmap for our distributed database kernel, including query execution engines, custom/hybrid columnar storage layers, distributed transaction managers, and cluster coordination protocols.
 * Low-Level Development: Write, optimize, and maintain highly performant, production-ready backend code utilizing modern C++ (C++20/23).
 * Parallel Execution Planning: Architect MPP execution engines that distribute, schedule, and execute queries across several dozens to hundreds of nodes using MPI and hybrid OpenMP models.
 * Hardware Optimization: Profile and eliminate execution bottlenecks by implementing explicit SIMD vectorization (AVX-512, ARM Neon, SVE) and ensuring absolute cache locality (L1/L2/L3).
 * Distributed Memory & Networks: Design ultra-low latency cluster communication subsystems utilizing RDMA, RoCE, or kernel-bypass tech (DPDK).
 * In-Database ML Framework Design: Architect and implement a native, highly performant, zero-copy ML execution framework directly inside the MPP engine to eliminate data serialization and transport bottlenecks between the database and external AI pipelines.
 * Functional & High-Performance Algorithms: Develop mathematically rigorous, cache-oblivious, and highly vectorized algorithms for real-time statistical computations, matrix operations/manipulations on clustered data
 * Vector & Embedding Infrastructure: Design and optimize or embed/reference distributed high-dimensional vector storage subsystems, custom SIMD-accelerated indexing structures (e.g., HNSW, IVF-PQ), and parallelized approximate nearest neighbor (ANN) search algorithms.
 * Be comfortable working in a cross country/multi time zone team of elite systems engineers, implementing as well as conducting rigorous peer design and development reviews
 * Collaborate with cross-functional teams in defining and developing new platform capabilities.
 * Actively participate in architecture reviews, code reviews, and technical mentoring.
 * Investigate and resolve complex performance and scalability issues., * Minimum - 4 years Computer Science engineering degree from a reputed institute. Masters in HPC (high performance computing) and Bigdata engineering areas preferred for Senior staff engineer and above roles