Agent Data Foundation ML Research Engineer

United States Digital Space LLC; New York, San Francisco (NY, CA); On-site; USD 265,000 - 331,000; Full time

Benefits offered by this job

Health insurance

Dental coverage

Vision coverage

Retirement benefits

Learning and development stipend

Generous PTO

Job summary

United States Digital Space LLC in New York is seeking researchers to advance AI through production-grade LLM work, synthetic data pipelines and agent-building technologies. You will develop data for reinforcement learning post-training and build agents from production traces to drive improvements across enterprise deployments.

The role emphasizes publishing in top venues, collaborating on proprietary algorithms, and training state-of-the-art models for customer delivery.

Qualifications

• 3+ years of building with LLMs in a production environment.
• Experience constructing high-quality data to improve an LLM/Agent.
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years.
• PhD or Masters in Computer Science or a related field.

Responsibilities

• Build synthetic data pipelines to generate enterprise environments for RL post-training.
• Create agents to convert traces from production into actionable insights.
• Contribute to our agent-building product using coding agents and proprietary algorithms.
• Train state-of-the-art models for deployment to enterprise customers.

Skills

LLM development

Data quality engineering

Research in AI/ML

Production-scale ML systems

Education

PhD or Masters in Computer Science or related field

Job description

United States Digital Space LLC in New York is seeking researchers to advance AI through production-grade LLM work, synthetic data pipelines and agent-building technologies. You will develop data for reinforcement learning post-training and build agents from production traces to drive improvements across enterprise deployments.

The role emphasizes publishing in top venues, collaborating on proprietary algorithms, and training state-of-the-art models for customer delivery.