Senior ML Engineer - Production LLM Systems

Dow Jones & Co.; New York (NY); On-site; USD 125,000 - 155,000; Full time

Job summary

Dow Jones is seeking a Senior Machine Learning Engineer to design and build the core systems behind our LLM applications, including orchestration, tooling, and evaluation harnesses for production reliability.

You will not be doing research or model training, but solving real production problems. Report to the Director, Data Science & Insights, and collaborate with cross-functional teams in NYC.

Qualifications

• At least 2+ years' experience in software/ML engineering, including at least 1 year building LLM systems in production.
• Expert Python; hands-on with LLM APIs and cloud platforms (AWS, Snowflake, Vertex).
• Experience orchestrating multistep LLM workflows (LangChain/LangGraph).
• Experience building agent harnesses: memory compression, skill disclosure, and tool design.
• Strong prompt engineering: tuning, versioning, and evaluation of prompts and model configs.
• Proven track record in AI evaluation, observability, and retrieval over enterprise data.
• Ability to explain complex AI concepts to nontechnical stakeholders.
• Fluency with agentic coding tools (Claude Code) and architecture patterns.

Responsibilities

• Design and build the orchestration, prompting, and workflow layers behind production AI features.
• Create composable tools and integrations, including retrieval and MCP servers and clients.
• Build evaluators, dashboards, and alerts that track accuracy, latency, cost, and hallucination.
• Harden the platform for production scale: caching, reliability, incidents, and model rollbacks.
• Set the technical bar through architecture decisions, design reviews, and mentoring.

Skills

Python

LLM workflows

LangChain

Prompt engineering

AI evaluation

Observability

Stakeholder communication

Claude Code

Tools

AWS

Snowflake

Vertex

LangGraph

Claude Code

Job description

Dow Jones is seeking a Senior Machine Learning Engineer to design and build the core systems behind our LLM applications, including orchestration, tooling, and evaluation harnesses for production reliability.

You will not be doing research or model training, but solving real production problems. Report to the Director, Data Science & Insights, and collaborate with cross-functional teams in NYC.