Machine Learning Platform Engineer
Description For Machine Learning Platform Engineer
Alembic, a pioneering company in marketing ROI analysis, is seeking a Machine Learning Platform Engineer to join their team in San Francisco. This role sits at the intersection of platform engineering and machine learning, requiring 8+ years of experience to build and scale the infrastructure powering their AI-driven Marketing Intelligence Platform.
The position offers a competitive compensation package ranging from $218K to $240K, plus equity, reflecting Alembic's commitment to market-leading compensation at or above the 75th percentile. This is a Staff-level role (IC5/IC6) that demands deep technical expertise in both platform engineering and ML operations.
As a Machine Learning Platform Engineer, you'll be responsible for designing and implementing the entire ML infrastructure ecosystem, from data ingestion to model deployment and monitoring. The role requires expertise in Python, Kubernetes, and modern ML frameworks, along with strong experience in cloud-native tooling and Infrastructure as Code. You'll work closely with data scientists and product teams to enable scalable training, deployment, and monitoring of models in production.
The ideal candidate will bring deep understanding of the ML lifecycle, strong expertise in monitoring and observability tools, and experience with ML workflow orchestration. You'll be implementing tools for ML experimentation, feature engineering, and training pipelines while ensuring robust CI/CD practices and maintaining high standards for model governance.
What makes this opportunity unique is the chance to directly impact how Fortune 500 companies make data-driven marketing decisions. You'll be working with a mission-driven team focused on solving complex causal inference problems and delivering reliable ML solutions at scale. The role offers the satisfaction of building infrastructure that enables better decision-making for enterprise customers while working in a collaborative environment that values impact, innovation, and integrity.
If you're passionate about MLOps, have a strong platform engineering background, and want to help revolutionize how companies understand their marketing ROI through AI, this role offers an excellent opportunity to make a significant impact while working with cutting-edge technology.
Responsibilities For Machine Learning Platform Engineer
- Design, build, and maintain infrastructure to support the full ML lifecycle
- Implement tools and workflows for ML experimentation, feature engineering, training pipelines, and hyperparameter tuning
- Build and run scalable and versioned deployment systems using modern frameworks
- Collaborate with data scientists to productionize ML-based applications
- Integrate CI/CD practices into the ML workflow
- Establish monitoring, alerting, and logging for models in production
- Champion best practices around reproducibility, traceability, and model governance
Requirements For Machine Learning Platform Engineer
- 8+ years of experience in platform engineering, with ML infrastructure or MLOps experience
- Deep understanding of the ML lifecycle, including data pipelines, training, model serving, and observability
- Strong experience with cloud and cloud-native tooling and Infrastructure as Code tools
- Deep expertise with monitoring and observability tools
- Familiarity with ML workflow orchestration
- Proficiency in Python and experience integrating with ML libraries and frameworks
- Strong collaboration skills and ability to support cross-functional data and ML teams
Benefits For Machine Learning Platform Engineer
- Competitive salary at or above 75th percentile
- Generous equity program
- Opportunity to shape and scale production ML platform
- Collaboration with data scientists and product teams
- Work with enterprise customers
- Mission-driven, collaborative team environment
