Senior Machine Learning Engineer

Summary

Join Marker Learning, a rapidly growing startup, as a Senior Machine Learning Engineer. You will play a pivotal role in empowering psychologists and clinicians with innovative tools that streamline assessments, diagnoses, reporting, and evaluation management. Lead the charge on productionizing AI features across parsing, summarization, and chat-based report editing. Own the full ML lifecycle, from prototype to production, and architect the infrastructure for reliable, fast, and impactful systems. Partner with other MLEs, product engineers, and domain experts to ensure LLM-based features are powerful, trustworthy, and integrated with user workflows. Contribute to a mission-driven team dedicated to providing reliable and affordable testing to every student who requires it.

Requirements

• 8+ years of overall engineering experience
• Strong track record in shipping production ML systems
• Deep experience in ML infrastructure, pipelines, and model serving
• Deep, hands-on experience with LLMs, retrieval-augmented generation, or document intelligence
• Strong Python and ML ecosystem fluency (e.g., PyTorch, LangChain, Weaviate, HuggingFace)
• Systems thinking—understands how models interact with APIs, databases, and frontend features
• Bias for action, pragmatic mindset, and thrives in a fast-moving startup environment

Responsibilities

• Own and scale the end-to-end ML lifecycle: data pipelines, training, serving, monitoring, and evaluation
• Build and productionize systems for: AI document parsing (OCR, classification, extraction)
• LLM-powered summarization and content generation
• Chat interfaces for editing special education reports
• Optimize model performance, latency, reliability, and cost
• Partner with engineers and PMs to deliver high-quality, user-facing AI features
• Establish best practices for LLM evaluations, structured prompt engineering, fallback strategies, and continuous improvement
• Raise the bar on technical design, testing, and observability across ML systems