Machine Learning Applications Engineer (GPU-Accelerated)

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