Member of Technical Staff, ML Product Engineering

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 RoleWe seek experienced engineers and scientists to bridge the gap between research and real-world applications by training and deploying our diffusion large language models. You'll build our core product offerings, partner with customers, and ensure our models perform reliably at scale in production environments.
Key Responsibilities

Design, develop, and optimize our models for production use cases.
Partner with customers to understand their requirements and translate them into technical solutions.
Implement innovative approaches for post-training generative AI models, including agentic workflows.
Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.
Contribute to the deployment and maintenance of models in production environments.
Collaborate with product teams to design and implement customer-facing ML features.

Qualifications

BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).
At least 5 years of experience working on ML projects in PyTorch (or equivalent), preferably in a research lab or engineering role.
Excellent familiarity with transformers and core LLM concepts (autoregressive pretraining, instruction tuning, in-context learning, LoRA, KV caching).
Experience training LLMs, including fine-tuning.
Familiarity with large-scale systems and high-performance computing, including GPU/TPU utilization.
Experience with version control (Git) and containerization (Docker).
Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.

Preferred Skills

Expertise in data engineering and synthetic data generation for LLMs.
Knowledge of MLOps and production-level deployment workflows.
Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT.
Experience with cloud platforms (AWS, GCP, Azure).
Experience with model quantization and optimization techniques.

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, ML Product Engineering at Inception Labs scores 98 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

AI AgentsFine TuningMl OpsAI EvaluationOpenAIvLLMPyTorchDatabricks

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
How do you decide that one model's output is better than another's for a given task?
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: AI Agents, Fine Tuning, Ml Ops, AI Evaluation, 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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