Member of Technical Staff, Forward Deployed AI Engineer

AI in this role

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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 RoleInception 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 RoleThis 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 FitThis 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.

Compensation

The annual base salary range for this role is $175,000 – $275,000 USD. Final compensation is determined based on experience, skills, and qualifications. Equity and benefits are included in the total package.
Why Join Inception

Work with World-Class Talent: Collaborate with the inventors of diffusion models and leading AI researchers
Shape Foundational Technology: Your decisions will influence how the next generation of AI products are built and used
Immediate Impact: Join at the ground floor where your contributions directly shape product direction and company trajectory

Perks & Benefits

Competitive salary and equity in a rapidly growing startup
Flexible vacation and paid time off (PTO)
Health, dental, and vision insurance
401k match
Catered meals (breakfast, lunch, & dinner)
Commuter subsidies
A collaborative and inclusive culture

About UsInception creates the world’s fastest, most efficient AI models. Today’s autoregressive LLMs generate tokens sequentially, which makes them painfully slow and expensive. Inception’s diffusion-based LLMs (dLLMs) generate answers in parallel. They are 5x faster and more efficient, while delivering best-in-class quality.
Inception was co-founded by Stanford professor Stefano Ermon, who co-invented such breakthrough AI technologies as diffusion models, flash attention, and DPO, UCLA professor Aditya Grover, who co-invented node2vec, decision transformers, and d1 reasoning, and Cornell professor and Afresh co-founder Volodymyr Kuleshov, who co-invented MDLM and Block Diffusion.
We pioneered the application of diffusion to language, with world’s first (and only) commercially available dLLM, Mercury. We are currently deploying our large-scale diffusion LLMs at Fortune 500 companies. Diffusion is the technology behind today’s image and video AI, and we’re making it the standard for LLMs as well.
Our team includes engineers from AWS, Google DeepMind, Meta AI, Microsoft, HashiCorp, and OpenAI. Based in Palo Alto, CA, we are backed by top-tier venture capitalists, including Menlo Ventures, Mayfield, M12 (Microsoft’s venture fund), Snowflake Ventures, Databricks, and Innovation Endeavors, and by tech luminaries such as Andrew Ng, Andrej Karpathy, and Eric Schmidt.
If you are talented, innovative, and ambitious, come help us invent the future of AI.We are an equal opportunity employer and encourage candidates of all backgrounds to apply.

How we score this

Member of Technical Staff, Forward Deployed AI Engineer at Inception Labs scores 90 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

Classification

AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.

AI Level 480 to 100
AI Level 360 to 79
AI Level 240 to 59
AI Level 10 to 39

Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

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Skills and AI tools this role asks for

RagAI AgentsAI EvaluationSpeechOpenAIDatabricks

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
How do you decide that one model's output is better than another's for a given task?
What have you built with speech recognition or text-to-speech, and where did it break?
Walk me through how you've used OpenAI in your day-to-day work.

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List these exact terms on your resume: Rag, AI Agents, AI Evaluation, Speech, and OpenAI. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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