Research on the largest, most capable foundation models — typically pre-release, large-scale, and exploratory; the work that defines next-generation model behavior.
Primary AI lifecycle stage: pre-training.
As of today, 390 active AI roles across 71 companies in our index reference Frontier research. Hiring concentrates at the pre-training (40%) and post-training (34%) stages. Most common sectors: Big Tech, AI Frontier, Semiconductors. New postings fell 29% in the last 30 days versus the prior 30 (112 → 80).
Research on the largest, most capable foundation models — typically pre-release, large-scale, and exploratory; the work that defines next-generation model behavior. Primary AI lifecycle stage: pre-training.
390 active AI roles across 71 companies in our index reference Frontier research as of today. New postings fell 29% in the last 30 days versus the prior 30 (112 → 80).
The companies with the most active Frontier research listings are: Amazon (44 roles), Meta (39 roles), NVIDIA (34 roles), Microsoft (32 roles), Google (28 roles).
Frontier research primarily belongs to the pre-training stage of the AI lifecycle. In current hiring, Frontier research roles concentrate at: pre-training (40%), post-training (34%).
The sectors with the most active Frontier research hiring are: Big Tech, AI Frontier, Semiconductors.
11 AI roles tagged frontier_research.
| Company | Title | Sector | AI score | Other tags |
|---|---|---|---|---|
| Uber | Senior Machine Learning Engineer - AV Foundation, AV Labs | Consumer | 10 | Multimodal · Pretraining · Fine-tuning |
| Whoop | Senior AI Researcher (Foundation AI) | Consumer | 9 | Multimodal · Pretraining · Fine-tuning · Model serving |
| Airbnb | Principal Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning | Consumer | 9 | Agent orchestration · Multi-agent · LLM observability · Multimodal · Recommender systems |
| Uber | Machine Learning Engineer II - AV Foundation, AV Labs | Consumer | 9 | Multimodal |
| DoorDash | AI Research Fellowship, (Summer and Fall 2026) | Consumer | 9 | Agent orchestration · Tool use · Evals · Forecasting · Multimodal · Vision · Audio & speech · Synthetic data |
| Uber | 2026 PhD Research Intern, India | Consumer | 9 | Fine-tuning · RLHF · Evals · Agent research |
| Uber | Senior Research Scientist, Generative AI | Consumer | 9 | RL post-training · Fine-tuning · Evals · Vision |
| Staff Research Engineer, Post-training & Evaluation | Consumer | 9 | Fine-tuning · Evals · LLM observability · RL post-training | |
| Spotify | Senior Research Scientist - Personalization | Consumer | 8 | Recommender systems · Evals |
| Discord | Manager, Scaled Abuse Countermeasures and Research | Consumer | 7 | Agent orchestration · Evals · Guardrails · LLM observability · RAG · Vector DB · Fine-tuning · Model serving · Recommender systems · Search & ranking · Vision · Audio & speech · Interpretability · Synthetic data · Agent research · RL post-training · RLHF · Reward modeling · RL robotics · Embodied AI |
| Whatnot | Software Engineer, Trust & Risk | Consumer | 7 | Agent orchestration · Evals · Guardrails · LLM observability · RAG · Vector DB · Fine-tuning · Inference infra · Model serving · Recommender systems · Search & ranking · Vision · Audio & speech · Interpretability · Synthetic data · Agent research · RL post-training · RLHF · Reward modeling · RL robotics · Embodied AI |