Senior Machine Learning Engineer (Nova)
9.0/10
Iterable
$140,000 – $200,000 USD
Remote
senior
27 days ago
aitechPythonTypeScriptMachine LearningLLMMastra
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Description
What you'll do
- •Design and build Machine Learning platform components that support agentic systems, including retrieval pipelines, indexing strategies, and model integration layers.
- •Introduce and operationalize RAG use cases, from data sourcing and embedding generation to runtime retrieval patterns.
- •Develop generalized evaluation frameworks for LLM- and agent-based features, including offline metrics, golden datasets, and continuous monitoring.
- •Implement abstractions, tooling, and reusable patterns that enable other teams to build ML- and LLM-powered experiences efficiently.
- •Partner with backend engineers to productionize ML features with strong reliability, observability, and performance characteristics.
- •Prototype applied ML solutions to validate feasibility before investing in full builds.
- •Ensure secure, robust handling of data used in ML workflows and retrieval operations.
- •Collaborate with product, design, and engineering to align ML system design with user experience and product goals.
- •Contribute to iterative improvements of the Nova agent framework, including workflows built with Mastra and TypeScript.
Conditions
- •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
Requirements
- •5+ years experience as a Machine Learning Engineer or similar role focused on production systems.
- •Strong engineering skills with Python or TypeScript, including experience building ML workflows in frameworks like Mastra or comparable agent/LLM toolkits.
- •Experience with retrieval systems, vector databases, search technologies, or RAG architectures.
- •Prior work integrating ML or LLM-powered features into production applications.
- •Understanding of ML evaluation techniques, experimentation design, and failure analysis.
- •Ability to lead complex projects, make practical trade-offs, and work independently in areas of ambiguity.
- •Strong communication and collaboration skills in a distributed environment.
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