
Senior Machine Learning Engineer
- London Denver, Norfolk
- Permanent
- Full-time
- Independently lead large-scale machine learning initiatives-delivering capabilities for scalable feature engineering, data processing, and model training on Databricks.
- Design, build, and deploy machine learning models that enable our partners to reach the right user with the right message at the right time.
- Own the complete lifecycle of ML platform features: from requirements gathering and architecture, through implementation, deployment, and post-launch support.
- Shape architectural decisions aimed at building robust, reusable, and highly available ML infrastructure that raises the bar for engineering and data science excellence.
- Mentor colleagues through code reviews, technical design sessions, and knowledge sharing, helping grow a strong culture of engineering rigor and learning.
- Have 5+ years of experience in machine learning engineering, data infrastructure, or platform engineering, preferably in a SaaS environment.
- Demonstrate a strong track record leading multi-stakeholder projects that deliver platform features, scalable ML tooling, or end-to-end training systems.
- Show proficiency with Python (with a preference for experience in distributed data processing environments like Databricks, Spark, or similar platforms).
- Bring hands-on experience with large-scale data pipelines, distributed systems, and cloud data storage (Databricks Delta, Spark, Kafka, Postgres, etc.).
- Exhibit a product-minded approach: comfortable partnering with product managers and data practitioners to balance trade-offs across usability, scalability, and complexity.
- Possess curiosity and adaptability to master new ML and data technologies, frameworks, and best practices.
- Communicate and collaborate effectively within remote and distributed teams.
- Experience building or operating ML platforms on Databricks.
- Scala development experience
- Familiarity with ML workflow orchestration tools (e.g., MLflow, Kubeflow, Airflow) and interest in automating model development, testing, and deployment.
- Exposure to generative AI or large language model workflows within an agentic or conversational UX context.
- Experience designing developer-facing APIs or tools to empower other ML engineers or data scientists.
- Success working in remote-first or globally distributed engineering organizations.
- Competitive salaries, meaningful equity, & 401(k) plan
- Medical, dental, vision, & life insurance
- Balance Days (additional paid holidays)
- Fertility & Adoption Assistance
- Paid Sabbatical
- Flexible PTO
- Monthly Employee Wellness allowance
- Monthly Professional Development allowance
- Pre-tax commuter benefits
- Complete laptop workstation
- Send job offers from free email services like Gmail, Yahoo mail, Hotmail, etc.
- Request money, fees, or payment of any kind from prospective candidates to apply to Iterable, for employment, or for the recruitment process (e.g. for home office supplies, or training, etc.).
- Request or require personal documents like bank account details, tax forms, or credit card information as part of the recruitment process prior to the candidate signing an engagement letter or an employment contract with Iterable.
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