Member of Technical Staff, LLM Evaluation Infra

AI in this role

openaipytorchdatabricks

ml-opsai-evaluationai-data-labeling

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 develop the evaluation infrastructure and systems that drive frontier LLM performance. You'll design the frameworks that tell us whether our models are improving and ensure they perform reliably at scale in production.
Key Responsibilities

Build scalable, automated evaluation pipelines that integrate into model training and deployment workflows.
Design, develop, and maintain robust evaluation frameworks and benchmarks for measuring LLM performance across diverse tasks and domains.
Conduct rigorous statistical analysis of model outputs to identify failure modes, biases, and performance gaps.
Partner with product and customer-facing teams to translate real-world use cases into meaningful evaluation criteria.
Define and implement quantitative metrics that capture model quality, safety, reliability, and regression detection.

Qualifications

BS/MS/PhD in Computer Science, Machine Learning, Statistics, or a related field (or equivalent experience).
At least 2 years of experience in ML evaluation, applied ML research, or a related engineering role.
Experience with version control (Git), containerization (Docker), and cloud services (AWS, GCP, or Azure).
Understanding of LLM fundamentals (autoregressive generation, instruction tuning, RLHF, in-context learning, decoding strategies).
Proficiency in Python and ML frameworks such as PyTorch.
Experience designing and implementing evaluation metrics and benchmarks for generative models.
Excellent communication skills with the ability to distill complex evaluation results into actionable insights.

Preferred Skills

Knowledge of existing benchmark suites and familiarity with agentic evaluations (such as SWE-bench or GPQA/GDPval) and their limitations.
Experience building evaluation infrastructure at scale using cloud platforms (AWS, GCP, Azure) and container orchestration tools (e.g., Kubernetes, Terraform).
Familiarity with MLOps practices and CI/CD pipelines for model validation and infrastructure deployment.
Experience with data engineering, large-scale data labeling, or synthetic data generation for evaluation purposes.
Familiarity with LLM safety and alignment evaluation (toxicity, hallucination detection, factual grounding).
Experience with human-in-the-loop evaluation systems (pairwise preference ranking, red-teaming).

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, LLM Evaluation Infra 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.

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

Ml OpsAI EvaluationAI Data LabelingOpenAIPyTorchDatabricks

Questions you could be asked

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?
How do you keep labeling instructions consistent across a large annotation team?
What's a project where you used OpenAI hands-on?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Ml Ops, AI Evaluation, AI Data Labeling, OpenAI, 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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