Senior Machine Learning Engineer

• Our Technology team drives the evolution of our Technology, Engineering, Data, Product and User Experience functions. With a keen focus on delivering cutting-edge solutions, we shape the digital landscape for our customers, readers and users
• From revolutionizing visuals to optimizing tools and harnessing the power of data, mobile, video and social platforms, our team is committed to providing a seamless and immersive experience across all touchpoints
• Collaborating closely with our newsrooms and strategic partners, we spearhead the development of groundbreaking products and technologies
• As Senior Machine Learning Engineer in the AI & ML foundations team, you will design and build the core systems behind our LLM applications: the orchestration layer, the reusable tooling and the evaluation harness that keeps it trustworthy in production
• In this role, you will not be applying AI research or model training - but to real problems in production. You will report to the Director, Data Science & Insights
• 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

Benefits

• 20 weeks of parental leave
• 4 weeks of paid caregiver leave
• Up to 4 weeks of remote work per year
• 1 paid volunteer day annually
• $700 caregiver reimbursement
• Headspace emotional wellness membership
• ClassPass fitness and wellness membership
• Financial rewards for well-being activities

• At least 2+ years’ experience in software/ML engineering - which includes at least 1 years’ experience building LLM systems in production
• Expert Python, hands on with LLM APIs and cloud platforms (AWS, Snowflake, Vertex)
• Fluency with agentic coding tools (e.g. Claude Code) and their architecture patterns (a strong plus)
• Ability to explain complex AI concepts and tradeoffs clearly to nontechnical stakeholders
• Experience building agent harnesses: memory compression, skill disclosure, and tool design
• A proven track record in AI evaluation, observability, and retrieval over enterprise data
• Deep experience orchestrating multistep LLM workflows as execution graphs (e.g. LangChain/LangGraph)
• Strong prompt engineering: tuning, versioning, and evaluation of prompts and model configs