San Francisco HQ
Full time
On-site
R&D
Our Compensation Philosophy:
Market-based: Our formula ensures new hires earn at or above current 75th percentile cash compensation benchmarks.
Ownership: Our generous equity program ensures new hires are owners, not just employees.
Transparent: We openly discuss salary expectations to avoid surprises later in the process.
Data-driven: We use objective data to remove bias and ensure consistency in compensation decisions.
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, youll 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
Were looking for a Machine Learning Applications Engineer with GPU, Python, and C++ expertise to help productionize cutting-edge causal AI models. Youll work closely with ML scientists to turn experimental research code into optimized, scalable, and well-structured software that powers Alembics real-time analytics and inference systems.
This is a hands-on, performance-focused role where youll 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 Alembics broader platform
Implement logging, profiling, and observability tools to track performance and model behavior
Must-Have Qualifications
47 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 Youll 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
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