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Member of Technical Staff, Forward Deployed AI Engineer

Inception

Location
Onsite (Bay Area, California)
Employment
Full-time
Level
Senior Level
Posted 2 days ago

About the Role

Inception is seeking Forward Deployed AI Engineers to help enterprise customers leverage diffusion-based language models. This role involves a blend of product engineering, customer implementation, and model optimization to deliver high-quality AI experiences.

Skills

Python Full-stack Development TypeScript JavaScript LLM Evaluation Prompt Optimization Agentic Workflows Enterprise Deployment RAG Model Tuning API Development Data Pipelines Customer Discovery Backend Systems ML Product Engineering Technical Pre-sales

Full job details

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 months.

As an early member of the team 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
  • Enterprise customer deployments: Work directly with strategic 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 LLM-as-judge workflows, evaluation harnesses, and feedback loops for customer-specific use cases.
  • Model and product optimization: Tune and customize Mercury models, prompts, workflows, and system architecture to meet customer-specific performance goals.
  • Agentic workflows: Build and optimize agentic workflows including subagents involving classification, routing, context compaction, search, coding agents, voice, and other latency-sensitive applications.
  • Build, prove, and generalize: Turn customer-specific deployments into repeatable product patterns, eval frameworks, implementation playbooks, and platform capabilities that improve Inception’s core product.


Qualifications
  • Strong engineering skills in Python and modern full-stack development, including APIs, backend systems, and ideally TypeScript/JavaScript.
  • Experience building, deploying, or integrating AI/LLM products with real users or customers.
  • Familiarity with LLM evaluation, LLM-as-judge workflows, data pipelines, model tuning, prompt optimization, or agentic workflows.
  • Customer-facing experience with enterprise, strategic, or high-value accounts.
  • Experience deploying software or AI systems in enterprise environments with security, privacy, reliability, compliance, or integration constraints.
  • Strong communication and discovery skills, with the ability to translate ambiguous customer needs into concrete technical solutions.
  • Ability to operate across engineering, product, sales, and customer success without requiring heavy process or handholding.
  • Willingness to work directly with customers in person when needed, including occasional travel for strategic deployments, workshops, and executive technical sessions.

Preferred Skills
  • Experience with RAG, search, voice AI, coding agents, or agentic workflow systems.
  • Experience deploying AI systems for Fortune 500 or large enterprise customers.
  • Track record owning technical pre-sales, post-sales, implementation, or customer expansion for million-dollar enterprise accounts.
  • Familiarity with LLM serving, latency optimization, model evaluation, or production ML systems.
  • Experience with data engineering, synthetic data generation, or feedback loops for model improvement.
  • Background in product engineering, ML product engineering, applied AI, or forward deployed engineering.
  • Experience working with customer-specific evals, benchmarks, and performance targets.
  • Familiarity with latency-sensitive applications, especially voice systems where response speed is critical.


A Note on the Role
This role is for builders who want to be close to customers and close to the product.

We are not looking for traditional solutions engineers who only configure demos, nor researchers who primarily want to work on open-ended model experiments. The strongest candidates are full-stack engineers with enough ML fluency to work across LLM systems, evals, data, tuning, deployment, and production application development — and enough customer instinct to discover what matters, build quickly, and drive real adoption.

This role is also not just about serving one-off customer requests. The best FDEs will identify repeatable patterns across deployments and turn those learnings into better product surfaces, platform capabilities, evals, playbooks, and model feedback loops.

A Note on Startup Fit
This is an in-office role at an early-stage company moving with high velocity. We're looking for engineers who are actively seeking a startup environment — comfortable with ambiguity, customer-facing work, rapid iteration, and end-to-end ownership.

The team is small and high-leverage. You should be excited to work directly with enterprise customers, own ambiguous problems, and build the systems that convert customer demand into production AI deployments.

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