---
title: 'Member of Technical Staff, Forward Deployed AI Engineer at Inception'
canonical: 'https://feeny.ai/job/member-of-technical-staff-forward-deployed-ai-engineer-inception-bay-area-n37pn7zzk4sr'
type: 'job'
last_seen: '2026-09-05'
---

# Member of Technical Staff, Forward Deployed AI Engineer at Inception

- **Company:** Inception
- **Location:** Bay Area
- **Compensation:** $175k–$275k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-05-22
- **Last confirmed live:** 2026-09-05
- **Apply:** https://jobs.gem.com/inception/am9icG9zdDpN10tXM3_1fH8n1od_h5MV

## Job description

## 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

- 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: 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

- BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).
- 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.

## About Inception

## Company Overview
- **One-liner**: Inception is an AI research and product company building diffusion-based language models (dLLMs) for production applications, offering 5x faster inference than traditional autoregressive LLMs.
- **Entity Type**: Private (Seed-stage; $57M total funding)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: Not publicly available (earliest funding round dated March 2025)
- **Founders**: Stefano Ermon (CEO), Aditya Grover (Co-Founder & CTO), Volodymyr Kuleshov (Co-Founder)

## Core Business
- **Primary industry**: Artificial intelligence research and infrastructure; large language models (LLMs)
- **Target customers**: B2B developers, enterprises, and AI application builders requiring low-latency, high-volume LLM inference for multi-step agents and real-time products.
- **Mission or purpose**: “We’re building the next generation of LLMs” – enabling intelligent, responsive, and scalable AI applications through diffusion-based architectures.

## Products & Services
- **Mercury (dLLM family)**: The world’s first commercially available family of diffusion large language models. Generates output via a coarse-to-fine iterative refinement process over a small number of steps, achieving 5x greater speed than autoregressive models while maintaining best-in-class quality. Seamlessly integrates into existing LLM workflows.
- **Research technologies**: d1 Reasoning, Discrete Diffusion Guidance, Direct Preference Optimization, Flash Attention, Decision Transformers, and Diffusion Models – foundational contributions that underpin the company’s product.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: Total funding of $57M across two seed rounds (2025)
- **Notable Investors / Partners**: Menlo Ventures (led the $50M seed round), Amazon Web Services (non‑equity assistance), Eric Schmidt (individual investor)
- **Growth Signals**: Headcount grew 187.5% year‑over‑year to 39 employees; LinkedIn followers increased 240% yearly to ~11,800; active job postings across AI systems, engineering, research, product, and marketing; talent sourced from Google DeepMind, Stanford SAIL, Meta, Amazon, and Pixxel.

## Competitive Advantages
- **Diffusion paradigm**: Non‑autoregressive generation that allows parallel refinement and revision during inference, breaking the sequential token‑by‑token bottleneck of conventional LLMs.
- **Speed‑cost curve**: Delivers 5x faster output with a fundamentally different cost structure, critical for real‑time, multi‑step agent workflows.
- **Founding team pedigree**: Co‑founders have pioneered breakthroughs in model architectures (Flash Attention, Direct Preference Optimization) and have deep experience turning research into production systems at scale.

## Strategic Focus
- Scaling Mercury adoption by integrating dLLMs into existing LLM pipelines and targeting high‑volume, latency‑sensitive applications.
- Advancing research in diffusion‑based reasoning, reinforcement learning for language models, and training/serving infrastructure.
- Expanding the team across AI systems (kernels, inference, training infra), product engineering, and research to accelerate product‑market fit.

## Why Work Here
- **Culture**: A tight‑knit team of scientists, engineers, and builders focused on shipping frontier AI research. Emphasis on innovation, speed, and real‑world impact.
- **Work policy**: In‑office for most roles (Palo Alto HQ); a Marketing Intern position is listed as remote. Typical time on‑site is full‑time in the office.
- **Notable perks / engineering culture**: Opportunity to work on cutting‑edge diffusion LLMs from scratch, contribute to foundational research (papers published), and collaborate with alumni from top AI labs. Roles span from kernel engineering to RL infrastructure and product management.
- **Growth**: Rapidly scaling headcount (+187% YoY) with open positions across multiple disciplines, offering strong career progression in a high‑visibility startup.

## Sources
1. [inceptionlabs.ai/about](https://www.inceptionlabs.ai/about)
2. [inceptionlabs.ai/careers](https://www.inceptionlabs.ai/careers)
3. [jobs.gem.com/inception](https://jobs.gem.com/inception)
4. [linkedin.com/company/inception-labs-ai](https://linkedin.com/company/inception-labs-ai)
5. [builtin.com/company/inception](https://builtin.com/company/inception)

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