Member of Technical Staff (Context Engineering)

About Contextual AI

We're revolutionizing how AI Agents work by solving AI's most critical challenge: context. The right context at the right time unlocks the accuracy and production scale that enterprises leveraging AI require. Our enterprise AI development platform sits at the intersection of breakthrough AI research and practical developer needs. Our end-to-end platform allows AI developers to easily and accurately ingest and query documents from enterprise data sources and easily embed retrieval results into their business workflows.  

Contextual AI was founded by the pioneers of Retrieval-Augmented Generation (RAG), the foundational technique behind the context layer, connecting foundation models to current and relevant information. Backed by the industry's most forward-thinking venture capitalists, we're not just participating in the enterprise AI revolution, we're defining it. Join us in building a future where AI doesn't just answer questions, it transforms businesses.

About the role

This role is designed to connect the dots across ingestion, retrieval and generation ensuring that the full context pipeline is optimized holistically, not just in isolated parts.

Contextual AI’s context layer technology enables enterprises to move beyond demos to actual production-grade AI applications. However, getting context layer models to deliver best results is still an art and requires working with a mixture of programming, instructing, finetuning and evaluation. As a Context Engineer, you will bring discipline to this process by applying a range of techniques (such as DSPy) to systematically discover and document prompting best practices for customer-specific workloads and beyond. Collaborating closely with research engineers and platform engineers, you will design and optimize prompts that guide context layer models to produce accurate, relevant, and contextually appropriate responses.

Given that context engineering is still a new domain, we suggest that you share with us any context engineering project on LLMs that you’re proud of working on. Ideally, showcase how you systematically used the techniques to improve and evaluate an LLM’s behavior. If you have experience with ML projects involving dataset curation and processing that will work as a substitute as well.

What you'll do

• Optimize ingestion configurations, chunking strategies, and metadata enrichment to support diverse document types and customer use cases.
• Design and refine agent templates, re-ranker and generator prompts, and access control policies to align with task objectives and security needs.
• Evaluate embeddings and retrieval pipelines across domains, using both automated scripts and human-guided analysis to determine effectiveness.
• Dogfood the platform regularly to surface issues and propose improvements across ingestion, retrieval, prompting, and tool use.
• Build high-quality demoware and internal agents to support the website, sales enablement, and customer onboarding.
• Benchmark the platform against competitors and help identify product differentiators and areas of improvement.
• Stay current on context engineering research and tooling; share insights internally to elevate platform quality and team practices.

What we're seeking

• Bachelor's degree in Computer Science, Engineering, or a related field (Master's degree preferred).
• Demonstrated expertise in NLP, particularly in the context of LLMs, with a deep understanding of model architectures, training methodologies, and evaluation metrics.
• Proficiency in programming languages commonly used in NLP, such as Python, along with experience working with deep learning frameworks like TensorFlow or PyTorch. Experience with DSPy and similar frameworks is a plus.
• Strong analytical and evaluation skills, with an ability to compare competing setups and identify what actually improves model outputs.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey complex concepts to non-technical stakeholders.
• Experience with evaluation tooling, version control systems (e.g., Git), and agile software development methodologies is a plus.
• Experience in the identification of vulnerabilities or security issues in LLMs or other AI models is a plus

Location: Mountain View, CA

Salary Range for California Based Applicants: $160,000 - $200,000 + equity + benefits (actual compensation will be determined based on experience, location, and other factors permitted by law).

Equal Opportunity
Contextual AI provides equal employment opportunities to all qualified individuals without regard to race, color, religion, sex, gender identity, sexual orientation, pregnancy, age, national origin, physical or mental disability, military or veteran status, genetic information, or any other protected classification. Equal employment opportunity includes but is not limited to, hiring, training, promotion, demotion, transfer, leaves of absence, and termination. Contextual AI takes allegations of discrimination, harassment, and retaliation seriously, and will promptly investigate when such behavior is reported.