About AlembicAlembic 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 RoleWe're looking for a
Machine Learning Applications Engineer to support our ML team in transforming experimental ideas into production-grade systems. This is a hands-on development role focused on turning early-stage causal inference models, analytics experiments, and prototypes into stable, maintainable, and performant code.
You'll work closely with data scientists, product engineers, and platform teams to accelerate the delivery of machine learning capabilities inside Alembic's product-without needing to be a hard-core ML researcher yourself.
Key Responsibilities- Translate early-stage ML notebooks, proofs-of-concept, and experiments into robust, testable, and modular Python code
- Optimize numerical and data-heavy code using Python tools such as Numba, Pandas, and NumPy
- Collaborate with ML scientists to improve the reproducibility, efficiency, and maintainability of research workflows
- Integrate ML-driven components into larger software systems with clean APIs and versioning practices
- Help scale inference pipelines by profiling, parallelizing, or caching performance-sensitive routines
- Write documentation and contribute to testing, logging, and monitoring for ML-influenced components
Must-Have Qualifications- 4-7 years of software development experience, with a focus on Python and data-centric systems
- Strong experience with numerical and analytical Python libraries like Pandas, NumPy, Numba, or SciPy
- Ability to work directly with ML prototypes and turn research code into clean, production-quality implementations
- Familiarity with software engineering best practices (modular design, testing, version control, etc.)
- Clear, structured communication skills and a collaborative mindset when working with researchers and engineers
Nice-to-Have- Interest in causal inference, marketing science, or experimentation platforms
- Familiarity with lightweight ML frameworks (e.g., Scikit-learn, XGBoost, LightGBM)
- Exposure to APIs and microservices for serving or integrating ML output
What You'll Get- A high-leverage role in helping bridge the gap between ML research and production systems
- The opportunity to shape how Alembic brings ML ideas to life across our platform
- Daily collaboration with world-class scientists and engineers
- A product-driven team culture that rewards curiosity, clarity, and execution
