Senior Staff Software Engineer, Serving
New
L
LiftoffPerformance Marketing
United States (Remote)Full-TimeStaff
SalarySF Bay Area, NYC, Los Angeles/Orange County: $255,000 - $310,000; Seattle/Olympia, Austin, San Diego, Santa Barbara, Boston: $234,000 - $285,000; All other cities and towns in our approved states: $219,000 - $266,000
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Job Details
- Experience
- 10+ years
- Required Skills
- Machine LearningAlgorithmsData StructuresGo
Requirements
- Strong core Computer Science fundamentals (data structures, algorithms, system architecture)
- 10+ years of industry experience
- M.S. or higher in Computer Science (or equivalent work experience)
- Experience with Go (plus)
Responsibilities
- Design, build, and operate the high-throughput, low-latency services that receive bid requests, execute Liftoff’s bidding logic, and respond to ad exchanges in real time.
- Improve the performance, scalability, and reliability of systems that process millions of requests per second under strict latency constraints.
- Develop and optimize GPU-powered inference services that execute neural network models using TensorRT.
- Profile end-to-end inference pipelines to identify and eliminate bottlenecks in GPU utilization, batching, memory access, serialization, networking, and request handling.
- Partner with machine learning engineers to productionize new model architectures and features, translating modeling requirements into efficient and reliable serving implementations.
- Design and operate large feature store fleets that provide low-latency access to real-time and precomputed features.
- Develop benchmarking and performance-analysis tools that help engineers compare models, understand latency and throughput trade-offs, and identify regressions before deployment.
- Improve experimentation tooling that allows machine learning teams to orchestrate offline training runs, evaluate candidate models, and maintain model leaderboards.
- Own changes through their full lifecycle—from system design and implementation to testing, deployment, observability, capacity planning, incident response, and continued optimization.
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