Member of Technical Staff, Forward Deployed AI Engineer

Inception creates the world’s fastest, most efficient AI models. Our Mercury model is the world’s fastest reasoning LLM and first commercially available diffusion LLM, delivering 5x greater speed and efficiency than today’s LLMs, with best-in-class quality. We are the AI researchers and engineers behind such breakthrough AI technologies as diffusion models, flash attention, and DPO. The Role Inception is hiring Forward Deployed AI Engineers to help enterprise customers deliver the highest quality AI experiences using our diffusion-based language models. This role sits at the intersection of product engineering, customer implementation, evals, data collection, model optimization, and enterprise deployment ownership. You will work directly with enterprise customers to identify high-value AI workflows, collect and structure customer data, build LLM-as-judge evaluation systems, tune model and product behavior for customer-specific goals, and turn fast proof-of-concepts into production deployments. This is not a traditional solutions engineering role, a pure research role, or a long-cycle consulting implementation role. We are looking for full-stack engineers who can operate close to customers, build real systems, communicate clearly, and move fast — including running fast POC cycles that take weeks to produce customer impact rather than exploratory research projects that take [... source excerpt omitted ...] responsible for turning Mercury models into high-value enterprise deployments and building the customer data flywheel that improves our models, products, and go-to-market motion. You will work closely with platform, serving, post-training, product engineering, and GTM teams to translate customer deployment learnings into model, product, and infrastructure improvements. Key Responsibilities Software engineering fundamentals: This is a software engineering role. Expect to spend 70%+ of your time writing code. You'll need strong command of CS fundamentals, including data structures and algorithms, to design and build reliable, scalable systems. Enterprise customer deployments: W [... source excerpt omitted ...] trategic enterprise customers to identify high-value AI workflows and turn them into production deployments. Rapid prototyping: Build and run fast proof-of-concepts, iterating on customer requirements and technical constraints on 2-week cycles. Production AI applications: Build full-stack AI applications, agentic workflows, integrations, internal tools, and customer-facing systems that bring Inception models into real enterprise environments. Data collection & feedback loops: Collect, structure, and operationalize customer data to improve model and product performance on customer use cases. Measurement and Evaluation: Define success metrics for customer deployments and design