Member of Technical Staff, Backend, LLM Applications

AI in this role

openaivllmdatabricks

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 RoleWe seek experienced backend engineers to own the systems that serve our diffusion LLMs in production. You'll build and operate infrastructure that handles billions of inference requests — optimizing for latency, throughput, cost, and reliability. This role sits at the intersection of ML systems and backend infrastructure.
Key Responsibilities

Design, build, and operate scalable backend services and model serving infrastructure for our diffusion LLMs.
Implement and manage load balancing, autoscaling, and traffic routing for model endpoints.
Build systems for model versioning, canary deployments, and zero-downtime rollouts.
Develop monitoring, alerting, and observability tooling to ensure SLA compliance and rapid incident response.
Benchmark and evaluate serving frameworks and hardware configurations to inform infrastructure decisions.

Qualifications

BS/MS/PhD in Computer Science or a related field (or equivalent experience).
5+ years of experience building production backend systems.
Strong proficiency in Python, including async programming and concurrent systems.
Solid understanding of distributed systems, networking, and load balancing at scale.
Familiarity with Kubernetes, CI/CD pipelines, and cloud infra (AWS and/or Azure).

Preferred Skills

Experience serving LLMs or other large generative models in production at scale.
Experience with cloud infrastructure (AWS, Azure), including GPU instance management and cost optimization.
Experience with infrastructure as code tools (Terraform) and deployment automation.
Experience with monitoring and observability tools (Prometheus, Grafana).
Familiarity with model serving frameworks (vLLM, Triton Inference Server, TensorRT-LLM).

Compensation

The annual base salary range for this role is $200,000 – $350,000 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, Backend, LLM Applications at Inception Labs scores 97 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.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

OpenAIvLLMDatabricks

Questions you could be asked

What's a project where you used OpenAI hands-on?
Walk me through how you've used vLLM in your day-to-day work.
What are the limits of Databricks that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: OpenAI, vLLM, and Databricks. 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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