Job Description
The Lead Machine Learning Engineer at Amperity will lead the development and deployment of scalable, reliable ML systems that enhance customer data platforms, working across teams to design infrastructure, optimize models, and implement best practices in MLOps within an AI-first environment.
Key Responsibilities
- Lead and guide ML projects across multiple teams, defining technical direction and platform capabilities.
- Collaborate with Applied Scientists, Software Engineers, Product, and Customer Success teams to develop production ML systems.
- Design and build ML platform components such as feature stores, model registries, and serving infrastructure.
- Develop automated training and deployment pipelines to support model improvement and handle data drift and model degradation.
- Create real-time and batch feature engineering systems for identity resolution, customer segmentation, and predictive modeling.
- Optimize model inference latency to meet service level agreements while managing infrastructure costs.
- Establish MLOps best practices, operational standards, and monitoring to ensure reliable and maintainable ML systems.
- Mentor engineers and influence the technical strategy for ML infrastructure and platform evolution.
Requirements
- 8 years of experience building production ML systems, with experience designing ML infrastructure and platforms.
- Technical leadership experience driving ML platform evolution or major ML projects across multiple teams.
- Expertise in ML deployment patterns, model serving, feature engineering, and monitoring observability for ML systems.
- Software engineering skills with experience in Python and familiarity with ML frameworks e.g. XGBoost, PyTorch, PySpark.
- Experience with cloud-native ML infrastructure, containerization, and orchestration tools such as Kubernetes and Docker.
- Experience in turning ambiguous ML infrastructure problems into actionable plans and guiding teams through delivery.
- Experience aligning ML technical strategy with organizational priorities and customer needs.
- Mentorship experience to engineers and improving how ML systems are built and operated.
- Ability to take ownership of ML systems end-to-end from model development through production operation.
- Experience using AI assistance tools like Claude Code to accelerate development and improve code quality.
- Knowledge of large-scale data engines such as Apache Spark, Presto, and Kafka.
- Familiarity with MLOps tooling including MLflow, feature stores, and model serving frameworks.
- Experience with cloud-native infrastructure built with Kubernetes and Terraform, deployed across multiple cloud providers.
- Proficiency in functional programming languages including Python (and Clojure is a plus) for ML pipelines.
- Experience developing machine learning models for entity resolution, classification, and customer analytics.
Benefits & Perks
Compensation/salary range: 190,000-260,000 USD base salary, with cash incentives and stock options
Work schedule: Hybrid work model with three days in the office each week
Work environment perks: Employee healthcare coverage, transportation subsidies, snacks, in-person and remote events and activities, self-managed PTO, flexible work arrangements
Additional benefits: Inclusive environment, opportunities for professional growth, equity grants, and a culture fostering curiosity, collaboration, and transparency
Ready to Apply?
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