Member of Technical Staff, Inference & Serving

Member of Technical Staff, Inference & Serving at Inception Labs scores 99 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.

San Mateo, United Statesleadfull_time

AI in this role

openaivllmpytorchtensorflowdatabricks

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're looking for engineers and scientists to design, optimize, and scale the systems that power our diffusion LLMs in production. Your work will make inference faster, more cost-effective, and more reliable.
Key Responsibilities

Build and optimize high-performance model serving systems for low-latency inference of diffusion LLMs.
Extend orchestration frameworks (Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving.
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.
Collaborate with ML researchers to translate model advances (new architectures, quantization techniques, batching strategies) into production-ready serving improvements.

Qualifications

BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience).
Knowledge of ML serving frameworks (SGLang, vLLM, Triton Inference Server, TensorRT-LLM).
Understanding of ML frameworks (PyTorch, TensorFlow) from a systems perspective.
Familiarity with high-performance computing and GPU programming (CUDA).
Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
Background in performance optimization and profiling of ML systems.

Preferred Skills

Experience building and maintaining large-scale language models with tens of billions of parameters or more.
Experience with distributed systems and cloud computing platforms (AWS/GCP/Azure).
Experience with ML workflow orchestration tools (Kubeflow, Airflow).
Experience with model optimization techniques (quantization, distillation, speculative decoding, continuous batching).
Knowledge of ML-specific infrastructure challenges (checkpointing, resource scheduling, etc.).

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.

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

OpenAIvLLMPyTorchTensorFlowDatabricks

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 PyTorch that you've run into, and how did you work around them?
What's a project where you used TensorFlow hands-on?
Walk me through how you've used Databricks in your day-to-day work.

Adapt your resume

List these exact terms on your resume: OpenAI, vLLM, PyTorch, TensorFlow, 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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