Member of Technical Staff, Reinforcement Learning

AI in this role

openaivllmpytorchdatabricks

fine-tuningai-evaluation

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 scientists and engineers with deep expertise in post-training large language models through reinforcement learning. You will design and implement RL training pipelines for our diffusion LLMs, develop reward modeling strategies, and build the algorithms that align model behavior with human intent at scale.
Key Responsibilities

Design, develop, and optimize RL training pipelines (PPO, DPO, RLHF, and novel approaches) for diffusion-based LLMs.
Build and iterate on reward models, reward shaping strategies, and evaluation of reward quality.
Implement innovative approaches for fine-tuning and scaling generative AI models.
Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.
Research and implement techniques for controlled text generation and constraint satisfaction.
Improve training stability, efficiency, and reproducibility of RL workloads.

Qualifications

BS/MS/PhD in Computer Science or a related field (or equivalent experience).
At least 2 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, KV caching).
Hands-on experience with reinforcement learning from human feedback (RLHF), PPO, DPO, or related post-training methods.
Familiarity with training and inference in diffusion models.
Experience training deep learning models at scale in distributed computing environments.

Preferred Skills

Extensive experience training transformer-based language models from scratch.
Experience designing and implementing reward models or preference learning systems.
Knowledge of advanced training techniques (mixed precision, gradient accumulation, etc.).
Background in optimization theory and neural network architecture design.
Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT.

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, Reinforcement Learning at Inception Labs scores 100 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

Fine TuningAI EvaluationOpenAIvLLMPyTorchDatabricks

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you decide that one model's output is better than another's for a given task?
What are the limits of OpenAI that you've run into, and how did you work around them?
What's a project where you used vLLM hands-on?
Walk me through how you've used PyTorch in your day-to-day work.

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List these exact terms on your resume: Fine Tuning, AI Evaluation, OpenAI, vLLM, and PyTorch. 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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