Machine Learning Engineer

Role overview

• check_circle Design and implement complex, multi-step agent pipelines using both open- and closed-source LLMs.
• check_circle Build tools and adapters that allow agents to interact securely with enterprise applications and APIs.
• check_circle Ensure agent behavior aligns with business objectives, reliability targets, and relevant compliance requirements.
• check_circle Create data generation, processing, and labeling pipelines to support model training and fine-tuning.
• check_circle Implement training and evaluation workflows for LLMs and downstream agent components.
• check_circle Run experiments to improve agent reasoning, tool-use, and task completion metrics.
• check_circle Operate and optimize inference endpoints and related serving infrastructure on cloud platforms.
• check_circle Implement monitoring, observability, and performance testing for deployed models and agents.
• check_circle Contribute to secure, scalable CI/CD for model release and rollbacks.

Basic qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or a related technical field.
• 2+ years of professional experience in software engineering, platform engineering, or ML engineering.
• Demonstrated experience building and shipping AI systems that leverage LLMs and agentic/tool-calling patterns.
• Strong background in ML pipelines, data engineering for model training, and vector/representation stores or equivalents.
• Proficiency in Python and common ML frameworks and tooling.
• Excellent problem-solving skills and experience working in fast-paced, collaborative teams.

Preferred qualifications

• Experience deploying production-grade, large-scale AI applications with attention to reliability and cost.
• Familiarity with model evaluation, observability, A/B testing, and post-deployment monitoring for AI.
• Strong communication skills for translating technical tradeoffs to non-technical stakeholders.
• The opportunity to build systems that materially improve how businesses operate using AI.
• High ownership and a direct influence on product and architecture decisions.
• A collaborative engineering culture focused on impact, quality, and rapid iteration.

About the company