Senior Staff Software Engineer, C++ Distributed Systems| Hybrid: Chicago, IL

Circana•,

38d•$190,000•Hybrid

About The Position

Circana is seeking Senior Staff Engineers to drive the design, evolution, and execution of its next-generation massively parallel processing (MPP) clustered database engine, Whitebox. The platform powers large-scale retail and consumer analytics for clients in more than 22 countries and serves millions of real-time queries against massive datasets. This highly technical role bridges high-performance computing, big data systems engineering, and low-latency machine-learning infrastructure, with a focus on database internals, distributed systems, performance engineering, and large-scale data processing. The engineer will be a primary designer and developer of low-latency, high-throughput distributed database kernels. The role includes designing bare-metal-optimized software layers so the database can use modern multicore, vectorized hardware and distributed network fabrics to process terabyte- and petabyte-scale datasets. It also involves enabling performant analytics and in-database machine-learning inference on relational and, occasionally, vector datasets. The position is based in a hybrid work arrangement in Chicago, Illinois. The posted salary range is up to $190,000 USD, and the role is also eligible for bonus pay. Circana offers paid time off, medical, dental, and vision insurance, and a 401(k) to eligible employees.

Requirements

A minimum four-year degree in Computer Science engineering from a reputed institute.
At least 8 years of hands-on production experience writing low-level systems in C++; C++17/20/23 is preferred.
Mastery of template metaprogramming, concepts, coroutines, and custom memory allocators.
Very good to expert-level knowledge of cluster-scale distributed programming with MPI, shared-memory threading with OpenMP, and MapReduce frameworks.
Documented experience with microarchitectural optimization, loop vectorization, pointer-aliasing constraints, and manual SIMD-intrinsics programming.
Deep understanding of operating-system internals, memory barriers, lock-free data structures, and atomics such as std::atomic.
Practical experience designing core database subsystems, such as 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 with one or more memory and compute monitoring or performance tools, such as Valgrind, Purify, or Intel, AMD, or ARM diagnostics.

Nice To Haves

A master’s degree in high-performance computing or big data engineering is preferred for Senior Staff Engineer and above roles.
A track record of open-source contributions or experience with proprietary distributed engines such as ClickHouse, DuckDB, Doris, CockroachDB, RocksDB, ScyllaDB, or Velox.
Experience with cloud-native distributed and elastic environments, object-storage tiering such as S3 or GCS, or zero-copy networking.
Experience integrating NVIDIA CUDA or ROCm acceleration into big-data workloads and hybrid CPU/GPU systems.
Experience building or modifying high-performance vector index or search engines, graph-based indices, or localized quantization frameworks.
Functional-domain awareness in CPG, retail, or pharma.

Responsibilities

Help shape core architecture and the roadmap for the distributed database kernel, including query execution engines, custom or hybrid columnar storage layers, distributed transaction managers, and cluster coordination protocols.
Write, optimize, and maintain production-ready backend code in modern C++.
Architect MPP execution engines that distribute, schedule, and execute queries across dozens to hundreds of nodes using MPI and hybrid OpenMP models.
Profile and remove execution bottlenecks through SIMD vectorization and cache-locality optimization.
Design low-latency cluster communication subsystems using RDMA, RoCE, or kernel-bypass technologies such as DPDK.
Architect and implement a native, high-performance, zero-copy machine-learning execution framework within the MPP engine.
Develop mathematically rigorous, cache-oblivious, vectorized algorithms for real-time statistical computations and matrix operations on clustered data.
Design, optimize, or integrate distributed high-dimensional vector storage subsystems, SIMD-accelerated indexing structures, and parallel approximate-nearest-neighbor search algorithms.
Work with a cross-country, multi-time-zone team of systems engineers, contributing implementation work and rigorous peer design and development reviews.
Collaborate with cross-functional teams to define and develop platform capabilities.
Participate in architecture reviews, code reviews, and technical mentoring.
Investigate and resolve complex performance and scalability issues.