Senior LLM Engineer, R&D

Title: Senior LLM Engineer, R&D Location: New York, NY

About Us:

Research & Development at The New York Times explores how emerging technologies can be applied in service of journalism. We collaborate with designers, developers, reporters, photographers and editors to bring new forms of storytelling to life, from prototyping to publishing.

At The Times, technology permeates every aspect of our business, from our thought-provoking coverage of technological trends to the digital-first transformation of our own business to the changing world of our readers. The Research & Development team explores the new opportunities that emerging technologies may provide the New York Times and our commercial and strategic goals.

We are looking for a Senior Software Engineer with a background in applied AI to develop applications using Large Language Models (LLMs) in service of The Times mission and journalistic standards.

You will report to the Executive Director of R&D.

Responsibilities:

• Design and implement LLM-backed APIs – driving improvements to reach target outcomes through evaluation, grounding and fine-tuning best practices.
• Deploy reliable services within cloud environments (such as GCP or AWS) to support high-traffic LLM applications.
• Manage the transition of features from experimental prototypes to stable production systems in a fast-paced environment with evolving requirements.
• Collaborate with designers, product managers and stakeholders to translate detailed requirements into technical tasks, providing accurate estimates and progress updates.
• Maintain comprehensive documentation to facilitate feature handoff and future maintenance.
• Stay at the forefront of the evolving AI and LLM landscape, identifying and advocating for the use of new technologies and best practices.
• Demonstrate understanding of our value of journalistic independence and a commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

You are comfortable building projects by yourself and enjoy collaboration with a larger team. You are both curious about and skeptical of new technologies. You're excited to apply new tools, but aim to do so in service of solving important problems and making a difference for users. You take pride in your craft and understand the need to capture patterns and support future development.

• 5+ years of professional software development or equivalent experience.
• Proficiency with Python and TypeScript/JavaScript.
• Professional experience architecting and shipping applications that use LLMs. Experience with multi-step chaining and human-in-the-loop workflows.
• Strong understanding of LLM evaluations. Experience refining products using instrumentation, monitoring, and golden datasets to ensure accuracy and robustness.
• Experience in algorithmic and AI text processing (e.g. text segmentation, semantic chunking, restructuring).

Preferred Qualifications:

• Experience building reliable, scalable back-end services in cloud environments (e.g. GCP, AWS).
• Familiarity with AI orchestration and evaluation frameworks (e.g. AWS Step Functions, Google Cloud Workflows, Dagster, Braintrust).
• Experience building front-end user interfaces for LLM-backed products.

To apply, please provide work samples (code snippets, URLs, project summaries) describing your most relevant work for this position and how it expresses your approach to software engineering.

This is a temporary position that is expected to last through November 2027.

#LI-Hybrid

REQ-020901

Additional Compensation and Benefits:

For roles in the U.S., dependent on your role, you may be eligible for variable pay, such as an annual bonus and restricted stock. Benefits may include medical, dental and vision benefits, Flexible Spending Accounts (F.S.A.s), a company-matching 401(k) plan, employee stock purchase plan, paid vacation, paid sick days, paid parental leave, tuition reimbursement and professional development programs.

For roles outside of the U.S., information on benefits will be provided during the interview process