Senior Software Engineer - Backend Performance -MarTech/AdTech

Three Pillars
San Francisco, CA, United States
27 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours

Tech stack

Artificial Intelligence Systems Engineering C++ (Programming Language) Profiling Nvidia CUDA Memory Management Python (Programming Language) PostgreSQL Unstructured Data Parquet Concurrency Backend
+2 more
Low Latency Vectorization

Job description

PLEASE CLICK HERE TO SEE ALL OF OUR JOB OPENINGS!Senior Software Engineer - Backend PerformanceAs 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 engineeringStrong 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 & dataParallel, 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 haveCUDA / 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. Job-3645252LI-AL1#LI-Onsite PLEASE CLICK HERE TO SEE *ALL OF OUR JOB OPENINGS!Senior Software Engineer - Backend PerformanceAs 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 engineeringStrong 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 & dataParallel, 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 haveCUDA / 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. Job-3645252*LI-AL1#LI-Onsite

Requirements

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.

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