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Join to apply for the Applications Engineer (GPU-Accelerated) role at Alembic Technologies

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Join to apply for the Applications Engineer (GPU-Accelerated) role at Alembic Technologies

This range is provided by Alembic Technologies. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$235,000.00/yr - $258,000.00/yr

About Alembic
Alembic is pioneering a revolution in marketing, proving the true ROI of marketing activities. The Alembic Marketing Intelligence Platform applies sophisticated algorithms and AI models to finally solve this long-standing problem. When you join the Alembic team, you’ll help build the tools that provide unprecedented visibility into how marketing drives revenue, helping a growing list of Fortune 500 companies make more confident, data-driven decisions.
About The Role
We’re looking for a Machine Learning Applications Engineer with GPU, Python, and C++ expertise to help productionize cutting-edge causal AI models. You’ll work closely with ML scientists to turn experimental research code into optimized, scalable, and well-structured software that powers Alembic’s real-time analytics and inference systems.
This is a hands-on, performance-focused role where you’ll operate at the intersection of applied ML, systems engineering, and high-performance computing.
Key Responsibilities

• Translate early-stage ML research and prototypes into reliable, testable, and performant software components
• Use CUDA, Triton, and Numba to optimize GPU-accelerated workloads for inference and preprocessing
• Contribute to core libraries and performance-critical routines using modern C++ in hybrid Python/C++ environments Develop modular, reusable infrastructure that supports deployment of ML workloads at scale Collaborate with data scientists and engineers to optimize data structures, memory usage, and execution paths
• Build interfaces and APIs to integrate ML components into Alembic’s broader platform Implement logging, profiling, and observability tools to track performance and model behavior
  

Must-Have Qualifications

• 4–7 years of software engineering experience, including substantial time in Python and C++
• Hands-on experience with GPU programming, including CUDA, Triton, Numba, or related frameworks Strong familiarity with the Python data stack (Pandas, NumPy, PyArrow) and low-level performance tuning Experience writing high-performance, memory-efficient code in C++
• Demonstrated ability to work cross-functionally with researchers, platform engineers, and product teams
• Comfort transforming research-grade ML code into maintainable, production-grade software
  

Nice-to-Have

• Experience with hybrid Python/C++ or Python/CUDA extension development (e.g., Pybind11, Cython, custom ops)
• Familiarity with ML serving or inference tools (e.g., TorchServe, ONNX Runtime, Triton Inference Server)
• Exposure to structured data modeling, causal inference, or large-scale statistical computation
• Background in distributed systems or parallel processing is a plus
  

What You’ll Get

• A pivotal role building GPU-accelerated software at the heart of a real-world AI product
• Collaboration with an elite team of ML scientists, engineers, and product leaders
• The opportunity to shape performance-critical infrastructure powering enterprise decision-making
• A culture rooted in technical rigor, curiosity, and product impact
  

Compensation Range: $235K - $258K

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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