AI Engineer, R&D

Church & Dwight is hiring an AI Engineer, R&D to join its R&D Agentic Pod team, delivering AI-powered capabilities that help R&D teams make decisions and operate more effectively. The role emphasizes reliable large language model and generative AI systems, with a focus on retrieval-augmented generation (RAG) and agentic workflows designed for production enterprise environments.

Role Focus

The AI Engineer, R&D will translate R&D business requirements into secure, scalable AI products that combine approved enterprise knowledge, structured and unstructured data, models, tools, permissions, and human oversight. The work includes building the technical foundations that enable agents and generative AI applications to operate reliably across enterprise systems.

Primary accountability includes building reliable RAG systems and agentic workflow orchestration integrated into production enterprises, with success measured through solution quality, accuracy, reliability, security, maintainability, user adoption, business impact, cost and latency performance, and delivery of reusable generative AI capabilities.

Key Responsibilities

• Convert R&D business needs into secure, scalable AI products using approved enterprise knowledge, structured and unstructured data, models, tools, permissions, and human oversight.
• Design, build, test, deploy, and maintain LLM and generative AI applications, agentic workflows, and orchestration patterns covering planning, routing, tool use, memory, handoffs, and human-in-the-loop controls.
• Build and optimize RAG systems using vector databases, embeddings, chunking, indexing, metadata, hybrid search, reranking, grounding, and source attribution, including traceable ingestion and refresh processes.
• Develop reusable production prompts and integrations for generative AI APIs, enterprise services, applications, analytical models, workflow tools, and knowledge repositories.
• Create automated and human evaluations for accuracy, groundedness, safety, relevance, task completion, latency, cost, and usability; monitor quality, adoption, incidents, and business outcomes.
• Identify hallucination, prompt injection, tool, retrieval, and operational risks; implement guardrails, fallbacks, authentication, authorization, logging, secrets management, and data controls.
• Apply strong software engineering practices, including modular design, source control, code review, automated testing, documentation, release management, observability, and production support.
• Partner with R&D, Quality, Regulatory, Data Science, GTA, architecture, data engineering, platform, security, privacy, and governance teams from discovery and prototyping through deployment, training, adoption, and continuous improvement.
• Support compliance with enterprise architecture, privacy, security, legal, compliance, data governance, and responsible AI standards by documenting architecture, controls, dependencies, procedures, limitations, and intended use.

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Engineering, Information Systems, or a related technical discipline.
• 3+ years of experience in software engineering, machine learning engineering, AI engineering, data engineering, or a related discipline.
• Hands-on experience building LLM and generative AI applications, including production prompt engineering, structured outputs, tool calling, model selection, APIs, evaluation, testing, monitoring, and optimization.
• Experience building enterprise RAG solutions using vector databases, embeddings, retrieval pipelines, knowledge sources, hybrid retrieval, reranking, grounding, and source attribution.
• Experience with agent frameworks, workflow orchestration, routing, handoffs, memory patterns, and human-in-the-loop controls.
• Proficiency in Python, REST APIs and SDKs, cloud AI services, source control, automated testing, authentication, and authorization.

Technical Skills

• Generative AI (LLMs, prompt engineering, structured outputs, tool calling, model optimization)
• RAG & Agents (embeddings, vector databases, hybrid search, reranking, grounding, orchestration, memory, human oversight)
• Engineering (Python, APIs, SDKs, cloud AI services, authentication, source control, automated testing, logging, secrets management)

Additional Competencies

• Action-oriented, data-driven problem solving focused on measurable outcomes.
• Strategic systems thinking with technical, business, and product acumen.
• Collaborative, curious, and accountable, with the ability to work effectively in ambiguity and influence without authority.

Education and Experience (Preferred)

• Master’s degree in Software Engineering, Data Science, Artificial Intelligence, Engineering, Information Systems, or a related technical discipline.
• Experience integrating AI solutions with enterprise applications, data platforms, security controls, and governed knowledge repositories.
• Experience in Consumer Packaged Goods R&D, Quality, and/or Regulatory environments.

Job Location and Work Environment

Princeton, NJ (Hybrid). In-person attendance is required Tuesdays, Wednesdays, and Thursdays, with remote work options on Mondays and Fridays.

Compensation and Benefits

• Base salary range: $71,000–$101,100 (U.S.)
• Bonus eligibility
• Medical, dental, vision
• Basic life insurance
• Paid vacation and sick time
• Paid Parental Leave
• Paid holidays, floating holidays, and vacation days starting in the first year of employment depending on hire date
• 401k retirement plan with company match and profit-sharing
• Discounted Employee Stock Purchase Plan

How Offer Amounts Are Determined

The actual base pay offered is based on job-related knowledge and skills, experience, business needs, geographical location, and internal pay parity.