Own the full ML lifecycle for product features, from ideation to deployment, observability, and iteration
Use off-the-shelf models (e.g. in AWS SageMaker, Bedrock, Comprehend, HuggingFace) to solve core user and business problems
Build and maintain robust, scalable ML pipelines and infrastructure (batch and real-time)
Work cross-functionally with product managers, designers, and backend engineers to ship ML-powered features
Drive productionization: model inference, latency tuning, monitoring, logging, and failure recovery
Continuously evaluate and integrate emerging models (especially LLMs) to improve accuracy and capabilities
Bring an engineering mindset to ML work, automate processes, reduce tech debt, and champion good software practices in the ML stack

5+ years of experience in machine learning engineering, ideally within a product-oriented tech team
1+ years of experience building GenAI/LLM-based features and products: chat bots, agents, features based on Gen AI
Strong software engineering background with experience writing clean, scalable, and maintainable code in Python
Hands-on experience using cloud-based ML services (especially AWS) and pre-trained models in production
Experience with modern deployment stacks and MLOps: CI/CD, model versioning, monitoring, etc
A pragmatic mindset—you value impact, simplicity, and reliability over academic perfection.

Competitive salary, equity, and benefits in a high-growth, mission-driven company

Machine Learning Engineer

Key skills