Forward Deployed Engineer

This is a fully remote position, open to applicants in New York.

📋 Description

• Collaborate closely with AI research teams and enterprise partners to establish research objectives, technical specifications, and project trajectory.

• Convert unclear AI and machine-learning challenges into well-defined technical initiatives.

• Serve as a technical implementation collaborator across research, engineering, product, and stakeholder teams.

• Navigate between research inquiries, technical frameworks, hands-on engineering, and partner-oriented execution.

• Manage technical systems from the discovery phase through implementation, deployment, iteration, and ongoing reliability.

• Create large-scale data-intelligence systems for the collection, organization, assessment, and enhancement of training and evaluation data.

• Develop ML pipelines for data curation, model training, assessment, experimentation, and continuous enhancement.

• Build infrastructure for model inference, experimentation, evaluation, and deployment.

• Design dependable workflows that support sophisticated AI research and production settings.

• Construct and sustain systems that manage complex data and machine-learning tasks.

• Develop LLM applications, including multi-agent systems, tool-utilizing agents, RAG workflows, and human-in-the-loop systems.

• Create evaluation harnesses and infrastructure for analyzing agent and model behavior.

• Develop systems that transition AI experiments into dependable, repeatable, multi-turn workflows.

• Implement agentic automation within technically intricate business and research processes.

• Utilize modern LLM tools and model-evaluation techniques for production-focused AI systems.

• Design data classifications, labeling systems, evaluation criteria, and quality frameworks.

• Enhance dataset structure and quality to bolster model performance and research results.

• Establish workflows for dataset curation, annotation, validation, and quality control.

• Analyze data and model behavior to pinpoint opportunities for system enhancement.

• Enforce strict standards for training data, evaluation datasets, and research processes.

• Project scope and research focus may change based on technical and partner needs.

⛳️ Requirements

• Significant professional experience as a software engineer, machine-learning engineer, applied AI engineer, or infrastructure engineer.

• Advanced Python programming skills with a track record of building and deploying production systems end to end.

• Hands-on experience with LLMs, agentic systems, multi-turn workflows, tool utilization, RAG, or AI automation.

• Experience in developing or managing data pipelines, ML infrastructure, model-evaluation systems, or research workflows.

• Strong grasp of data quality, taxonomy development, labeling workflows, and dataset curation for AI systems.

• Capability to work independently in ambiguous, partner-facing contexts with robust technical and product ownership.

• Comfortable collaborating directly with researchers, technical partners, founders, and enterprise stakeholders.

• Background in a startup, AI infrastructure company, applied AI organization, or research-driven engineering team is a plus.

• Experience in building multi-turn agents, agent-evaluation systems, workflow automation, or human-in-the-loop AI is advantageous.

• Familiarity with contemporary LLM tools, agent frameworks, model-evaluation stacks, and ML experimentation platforms is beneficial.

• Experience serving as a technical partner to external customers, research teams, or strategic enterprise accounts is highly valued.

• All work must be completed without utilizing confidential or proprietary information belonging to any employer, client, institution, or other third party.

🏝️ Benefits

• Remote work.

• Travel required as needed for partner-facing collaboration and project execution.