Machine Learning Engineer

Summary

Join Spendesk as a Machine Learning Engineer and play a key role in designing, building, and scaling ML-driven features and infrastructure, including the adoption of GenAI and LLMs. You will build scalable infrastructure for training, deploying, and monitoring machine learning models. Collaborate with product and engineering teams to deliver impactful ML-powered features. Research and engineer LLMs and GenAI solutions to enhance OCR capabilities and unlock innovative client features. Continuously evaluate and adopt new tools and technologies to improve the AI engineering stack. Monitor the performance and reliability of AI models in production. Spendesk offers a flexible remote policy, health insurance, wellness programs, and other benefits.

Requirements

• MS in Computer Science, Mathematics, or a related field
• 3+ years of experience as a Machine Learning Engineer or Software Engineer with ML/AI focus
• Strong programming skills in SQL and Python
• Hands-on experience working with LLMs
• Experience deploying and operating Machine Learning models in production
• Strong communication skills, able to work effectively with engineers, product managers, and other stakeholders

Responsibilities

• Design, implement, and maintain robust ML systems, APIs, and pipelines in production environments
• Build scalable infrastructure for training, deploying and monitoring machine learning models, including LLMs and genAI solutions
• Work closely with product and engineering teams to define clear requirements and deliver impactful ML-powered features (e.g. automated document processing, transaction categorization, fraud and anomaly detection)
• Research, experiment with, and engineer LLMs and GenAI (agents) solutions to improve our OCR capabilities and unlock innovative client features
• Continuously evaluate and adopt new tools and technologies to improve our AI engineering stack and workflows
• Monitor performance and reliability of AI models in production, ensuring robust and scalable operations

Preferred Qualifications

Familiarity with ML Ops tooling (Docker, Kubernetes, etc) and cloud platforms (AWS, GCP, etc)

Benefits

• Flexible remote policy
• Lunch 60% funded by Spendesk (Swile Card)
• Alan Premium health insurance
• A Gymlib pass to let off steam after a productive day at work
• Access to Moka.care for emotional and mental health wellbeing
• Access to Vendredi allowing us to change the world
• Latest Apple equipment
• Great office snacks to fuel your day
• A positive team to work with daily!