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Senior Software Engineer - Backend Performance -MarTech/AdTech
San Francisco, California, United States
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Senior Software Engineer — Backend Performance


As a Senior Software Engineer on Backend Performance, you own the hottest paths in data products — the code that has to be fast because everything downstream depends on it. You profile before you guess, drop into C/C++, Cython, or Rust when Python runs out of room, and reach for the GPU when it earns its keep. This is engineers-first, systems-heavy work: high-throughput pipeline execution and Reactor self-correction over large volumes of structured and unstructured data, made fast.


What You'll Do:

  • Profile, benchmark, and eliminate bottlenecks across the pipeline's performance-critical paths.
  • Write performance-critical code in C/C++, Cython, and/or Rust for the hot paths where interpreted Python won't hold.
  • Apply GPU acceleration (CUDA) to pipeline execution and Reactor self-correction where it delivers real speedups — and know when it doesn't.
  • Engineer for parallelism and concurrency: threading, vectorization, memory layout, and data-movement costs.
  • Build performance-regression detection into CI so hard-won gains don't quietly erode.
  • Partner with the Data Products and infrastructure teams to move the right work to the right primitive.


What Will Help You Succeed:

Core engineering

  • Strong systems engineering background with deep proficiency in at least one of C, C++, or Rust. Cython and CUDA are a strong plus.
  • Fluent in Python for data processing, with a real feel for where the interpreter costs you and how to escape it.
  • Performance engineering: profiling, memory management, concurrency, cache behavior, and reasoning quantitatively about throughput and latency.


Systems & data

  • Parallel, high-throughput processing — SIMD / vectorization, multiprocessing, or GPU parallelism.
  • Experience moving and transforming large volumes of structured and unstructured data with low latency.
  • PostgreSQL and strong data-structures and algorithms fundamentals.


Nice to have

  • CUDA / kernel-level optimization or hardware-aware performance work; NVIDIA ecosystem depth.
  • Lakehouse internals, columnar formats (Arrow / Parquet), or numeric/array computing.


The role is right for you if:

  • You reach for a profiler before another machine, and you can explain exactly where your time is going.
  • You write the systems code yourself. AI assistants speed you up; they don't do the engineering for you.

 

 

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