Showing 15801 tagged AI roles. Layout is a Fruchterman-Reingold force simulation, run server-side to convergence.
frontier_research ↔ pretraining (207 JDs) frontier_research ↔ interpretability (65 JDs) frontier_research ↔ multimodal (231 JDs) interpretability ↔ pretraining (14 JDs) multimodal ↔ pretraining (127 JDs) interpretability ↔ multimodal (33 JDs) fine_tuning ↔ frontier_research (259 JDs) fine_tuning ↔ multimodal (722 JDs) frontier_research ↔ vision (98 JDs) evals ↔ frontier_research (155 JDs) multimodal ↔ vision (587 JDs) evals ↔ multimodal (440 JDs) interpretability ↔ vision (20 JDs) evals ↔ vision (158 JDs) fine_tuning ↔ vision (314 JDs) evals ↔ interpretability (70 JDs) fine_tuning ↔ interpretability (53 JDs) evals ↔ fine_tuning (1104 JDs) frontier_research ↔ rl_post_training (162 JDs) agent_research ↔ rl_post_training (146 JDs) llm_observability ↔ rl_post_training (161 JDs) fine_tuning ↔ rl_post_training (356 JDs) agent_research ↔ frontier_research (151 JDs) frontier_research ↔ llm_observability (88 JDs) agent_research ↔ llm_observability (517 JDs) agent_research ↔ fine_tuning (285 JDs) fine_tuning ↔ llm_observability (1342 JDs) interpretability ↔ llm_observability (39 JDs) evals ↔ llm_observability (1787 JDs) llm_observability ↔ multimodal (436 JDs) agent_orchestration ↔ interpretability (31 JDs) agent_research ↔ interpretability (36 JDs) audio_speech ↔ interpretability (13 JDs) interpretability ↔ rl_robotics (12 JDs) interpretability ↔ model_serving (38 JDs) agent_orchestration ↔ agent_research (878 JDs) agent_orchestration ↔ audio_speech (139 JDs) agent_orchestration ↔ rl_robotics (64 JDs) agent_orchestration ↔ model_serving (3304 JDs) agent_orchestration ↔ evals (2004 JDs) agent_research ↔ audio_speech (31 JDs) agent_research ↔ rl_robotics (40 JDs) agent_research ↔ model_serving (354 JDs) agent_research ↔ evals (403 JDs) audio_speech ↔ rl_robotics (24 JDs) audio_speech ↔ model_serving (308 JDs) audio_speech ↔ evals (129 JDs) model_serving ↔ rl_robotics (75 JDs) evals ↔ rl_robotics (39 JDs) evals ↔ model_serving (1528 JDs) agent_research ↔ embodied_ai (40 JDs) agent_orchestration ↔ embodied_ai (131 JDs) embodied_ai ↔ rl_robotics (114 JDs) embodied_ai ↔ vision (77 JDs) embodied_ai ↔ multimodal (110 JDs) embodied_ai ↔ fine_tuning (78 JDs) agent_research ↔ vision (58 JDs) agent_research ↔ multimodal (176 JDs) agent_orchestration ↔ vision (195 JDs) agent_orchestration ↔ multimodal (668 JDs) agent_orchestration ↔ fine_tuning (1452 JDs) rl_robotics ↔ vision (37 JDs) multimodal ↔ rl_robotics (48 JDs) fine_tuning ↔ rl_robotics (50 JDs) evals ↔ synthetic_data (153 JDs) frontier_research ↔ synthetic_data (46 JDs) embodied_ai ↔ frontier_research (37 JDs) audio_speech ↔ frontier_research (60 JDs) pretraining ↔ rl_post_training (62 JDs) audio_speech ↔ pretraining (28 JDs) multimodal ↔ rl_post_training (140 JDs) audio_speech ↔ rl_post_training (56 JDs) audio_speech ↔ multimodal (197 JDs) fine_tuning ↔ pretraining (176 JDs) evals ↔ pretraining (41 JDs) agent_orchestration ↔ tool_use (2141 JDs) agent_orchestration ↔ guardrails (1536 JDs) evals ↔ tool_use (659 JDs) guardrails ↔ tool_use (576 JDs) embodied_ai ↔ tool_use (13 JDs) rl_robotics ↔ tool_use (8 JDs) agent_research ↔ tool_use (260 JDs) evals ↔ guardrails (1001 JDs) embodied_ai ↔ evals (77 JDs) embodied_ai ↔ guardrails (14 JDs) guardrails ↔ rl_robotics (13 JDs) agent_research ↔ guardrails (290 JDs) model_serving ↔ multimodal (938 JDs) inference_infra ↔ multimodal (480 JDs) inference_infra ↔ model_serving (4490 JDs) frontier_research ↔ model_serving (209 JDs) frontier_research ↔ inference_infra (95 JDs) agent_research ↔ pretraining (42 JDs) pretraining ↔ vision (43 JDs) multimodal ↔ synthetic_data (123 JDs) pretraining ↔ synthetic_data (20 JDs) fine_tuning ↔ synthetic_data (144 JDs) rl_post_training ↔ synthetic_data (78 JDs) agent_research ↔ synthetic_data (54 JDs) code_gen ↔ frontier_research (29 JDs) agent_research ↔ code_gen (55 JDs) code_gen ↔ rl_post_training (28 JDs) code_gen ↔ llm_observability (113 JDs) frontier_research ↔ guardrails (46 JDs) frontier_research ↔ rlhf (20 JDs) guardrails ↔ interpretability (46 JDs) interpretability ↔ rlhf (12 JDs) evals ↔ rlhf (39 JDs) guardrails ↔ rlhf (16 JDs) interpretability ↔ rl_post_training (44 JDs) evals ↔ rl_post_training (270 JDs) guardrails ↔ rl_post_training (96 JDs) model_serving ↔ pretraining (150 JDs) inference_infra ↔ pretraining (85 JDs) llm_observability ↔ pretraining (31 JDs) agent_orchestration ↔ rl_post_training (194 JDs) agent_orchestration ↔ frontier_research (100 JDs) multimodal ↔ tool_use (153 JDs) rl_post_training ↔ tool_use (70 JDs) frontier_research ↔ tool_use (20 JDs) embodied_ai ↔ pretraining (8 JDs) agent_orchestration ↔ rag (2827 JDs) agent_orchestration ↔ vector_db (996 JDs) agent_orchestration ↔ llm_observability (3855 JDs) rag ↔ vector_db (1228 JDs) evals ↔ rag (1037 JDs) llm_observability ↔ rag (2357 JDs) model_serving ↔ rag (1920 JDs) evals ↔ vector_db (351 JDs) llm_observability ↔ vector_db (913 JDs) model_serving ↔ vector_db (871 JDs) llm_observability ↔ model_serving (2756 JDs) rag ↔ tool_use (956 JDs) model_serving ↔ tool_use (866 JDs) inference_infra ↔ rl_robotics (43 JDs) embodied_ai ↔ inference_infra (95 JDs) embodied_ai ↔ model_serving (172 JDs) inference_infra ↔ vision (300 JDs) model_serving ↔ vision (508 JDs) fine_tuning ↔ inference_infra (795 JDs) fine_tuning ↔ model_serving (2113 JDs) agent_orchestration ↔ inference_infra (1270 JDs) evals ↔ inference_infra (534 JDs) llm_observability ↔ tool_use (1297 JDs) fine_tuning ↔ tool_use (406 JDs) embodied_ai ↔ llm_observability (23 JDs) fine_tuning ↔ rag (1155 JDs) rag ↔ rl_post_training (49 JDs) embodied_ai ↔ rag (10 JDs) embodied_ai ↔ rl_post_training (21 JDs) fine_tuning ↔ rlhf (47 JDs) fine_tuning ↔ vector_db (520 JDs) frontier_research ↔ vector_db (40 JDs) interpretability ↔ vector_db (19 JDs) rlhf ↔ vector_db (19 JDs) tool_use ↔ vector_db (297 JDs) multimodal ↔ rag (264 JDs) multimodal ↔ vector_db (115 JDs) inference_infra ↔ rag (686 JDs) inference_infra ↔ vector_db (410 JDs) inference_infra ↔ llm_observability (1113 JDs) inference_infra ↔ quantization (102 JDs) model_serving ↔ quantization (100 JDs) fine_tuning ↔ quantization (45 JDs) agent_orchestration ↔ quantization (6 JDs) reward_modeling ↔ rl_post_training (50 JDs) fine_tuning ↔ reward_modeling (41 JDs) reward_modeling ↔ synthetic_data (18 JDs) agent_orchestration ↔ reward_modeling (24 JDs) multimodal ↔ reward_modeling (22 JDs) rl_post_training ↔ vision (34 JDs) evals ↔ reward_modeling (40 JDs) reward_modeling ↔ tool_use (13 JDs) reward_modeling ↔ vision (11 JDs) audio_speech ↔ reward_modeling (9 JDs) tool_use ↔ vision (32 JDs) audio_speech ↔ tool_use (34 JDs) audio_speech ↔ vision (127 JDs) inference_infra ↔ interpretability (16 JDs) code_gen ↔ inference_infra (36 JDs) agent_research ↔ inference_infra (126 JDs) code_gen ↔ model_serving (85 JDs) code_gen ↔ interpretability (5 JDs) agent_orchestration ↔ search_ranking (198 JDs) rag ↔ search_ranking (257 JDs) model_serving ↔ search_ranking (436 JDs) fine_tuning ↔ search_ranking (162 JDs) agent_research ↔ rag (264 JDs) multimodal ↔ quantization (24 JDs) embodied_ai ↔ quantization (4 JDs) guardrails ↔ rag (678 JDs) fine_tuning ↔ guardrails (472 JDs) guardrails ↔ model_serving (860 JDs) guardrails ↔ llm_observability (1442 JDs) llm_observability ↔ vision (120 JDs) agent_orchestration ↔ code_gen (220 JDs) code_gen ↔ tool_use (106 JDs) inference_infra ↔ tool_use (284 JDs) model_serving ↔ rl_post_training (213 JDs) agent_orchestration ↔ multi_agent (137 JDs) agent_orchestration ↔ recommender_systems (322 JDs) llm_observability ↔ multi_agent (63 JDs) multi_agent ↔ multimodal (8 JDs) multi_agent ↔ recommender_systems (7 JDs) frontier_research ↔ multi_agent (7 JDs) llm_observability ↔ recommender_systems (301 JDs) multimodal ↔ recommender_systems (113 JDs) frontier_research ↔ recommender_systems (35 JDs) code_gen ↔ evals (101 JDs) guardrails ↔ multimodal (145 JDs) guardrails ↔ vision (29 JDs) audio_speech ↔ guardrails (30 JDs) rag ↔ vision (65 JDs) audio_speech ↔ rag (49 JDs) audio_speech ↔ llm_observability (119 JDs) embodied_ai ↔ synthetic_data (53 JDs) agent_orchestration ↔ synthetic_data (102 JDs) synthetic_data ↔ vision (59 JDs) rl_robotics ↔ synthetic_data (24 JDs) frontier_research ↔ rag (32 JDs) pretraining ↔ rag (8 JDs) guardrails ↔ vector_db (316 JDs) guardrails ↔ inference_infra (399 JDs) audio_speech ↔ fine_tuning (212 JDs) audio_speech ↔ inference_infra (166 JDs) model_serving ↔ synthetic_data (109 JDs) inference_infra ↔ synthetic_data (56 JDs) multi_agent ↔ rag (48 JDs) evals ↔ multi_agent (33 JDs) rag ↔ recommender_systems (253 JDs) inference_infra ↔ rl_post_training (98 JDs) llm_observability ↔ reward_modeling (24 JDs) model_serving ↔ reward_modeling (24 JDs) inference_infra ↔ reward_modeling (12 JDs) rag ↔ reward_modeling (12 JDs) frontier_research ↔ quantization (5 JDs) distillation ↔ inference_infra (12 JDs) distillation ↔ model_serving (13 JDs) distillation ↔ fine_tuning (11 JDs) distillation ↔ frontier_research (2 JDs) distillation ↔ quantization (12 JDs) code_gen ↔ multimodal (37 JDs) code_gen ↔ fine_tuning (65 JDs) fine_tuning ↔ multi_agent (29 JDs) agent_research ↔ multi_agent (19 JDs) multi_agent ↔ tool_use (27 JDs) inference_infra ↔ multi_agent (9 JDs) model_serving ↔ multi_agent (41 JDs) fine_tuning ↔ recommender_systems (281 JDs) model_serving ↔ recommender_systems (708 JDs) interpretability ↔ tool_use (6 JDs) interpretability ↔ rag (19 JDs) rl_post_training ↔ search_ranking (31 JDs) evals ↔ search_ranking (139 JDs) agent_research ↔ search_ranking (30 JDs) frontier_research ↔ search_ranking (22 JDs) llm_observability ↔ search_ranking (225 JDs) agent_research ↔ vector_db (104 JDs) code_gen ↔ rag (61 JDs) rl_post_training ↔ vector_db (38 JDs) vector_db ↔ vision (28 JDs) audio_speech ↔ vector_db (19 JDs) code_gen ↔ vector_db (23 JDs) code_gen ↔ vision (15 JDs) audio_speech ↔ code_gen (14 JDs) code_gen ↔ guardrails (28 JDs) recommender_systems ↔ vision (48 JDs) agent_research ↔ recommender_systems (46 JDs) quantization ↔ rl_post_training (5 JDs) recommender_systems ↔ tool_use (63 JDs) evals ↔ recommender_systems (185 JDs) recommender_systems ↔ vector_db (116 JDs) search_ranking ↔ vector_db (142 JDs) recommender_systems ↔ search_ranking (709 JDs) code_gen ↔ embodied_ai (4 JDs) multi_agent ↔ rl_post_training (9 JDs) pretraining ↔ vector_db (23 JDs) multimodal ↔ training_infra (8 JDs) audio_speech ↔ synthetic_data (60 JDs) pretraining ↔ recommender_systems (16 JDs) inference_infra ↔ recommender_systems (317 JDs) inference_infra ↔ search_ranking (212 JDs) llm_observability ↔ quantization (4 JDs) llm_observability ↔ rlhf (22 JDs) guardrails ↔ reward_modeling (12 JDs) model_serving ↔ rlhf (24 JDs) rag ↔ rlhf (20 JDs) reward_modeling ↔ vector_db (10 JDs) multimodal ↔ rlhf (23 JDs) rl_post_training ↔ rlhf (17 JDs) reward_modeling ↔ rlhf (19 JDs) audio_speech ↔ quantization (6 JDs) guardrails ↔ multi_agent (14 JDs) fine_tuning ↔ training_infra (6 JDs) model_serving ↔ training_infra (39 JDs) audio_speech ↔ training_infra (3 JDs) training_infra ↔ vision (2 JDs) search_ranking ↔ tool_use (36 JDs) guardrails ↔ recommender_systems (73 JDs) guardrails ↔ search_ranking (46 JDs) recommender_systems ↔ rl_post_training (42 JDs) synthetic_data ↔ tool_use (18 JDs) agent_research ↔ reward_modeling (15 JDs) rag ↔ synthetic_data (33 JDs) agent_orchestration ↔ pretraining (27 JDs) distillation ↔ rl_post_training (3 JDs) evals ↔ quantization (3 JDs) distillation ↔ evals (3 JDs) pretraining ↔ quantization (6 JDs) distillation ↔ pretraining (2 JDs) quantization ↔ vision (13 JDs) rlhf ↔ vision (12 JDs) audio_speech ↔ rlhf (9 JDs) embodied_ai ↔ rlhf (9 JDs) agent_orchestration ↔ rlhf (31 JDs) rlhf ↔ tool_use (11 JDs) audio_speech ↔ embodied_ai (7 JDs) multimodal ↔ search_ranking (70 JDs) pretraining ↔ tool_use (5 JDs) distillation ↔ multimodal (5 JDs) llm_observability ↔ synthetic_data (66 JDs) code_gen ↔ synthetic_data (11 JDs) llm_observability ↔ training_infra (6 JDs) frontier_research ↔ reward_modeling (16 JDs) agent_research ↔ rlhf (22 JDs) llm_observability ↔ rl_robotics (16 JDs) pretraining ↔ search_ranking (8 JDs) interpretability ↔ synthetic_data (18 JDs) interpretability ↔ reward_modeling (9 JDs) code_gen ↔ pretraining (13 JDs) rl_robotics ↔ sim2real (4 JDs) multi_agent ↔ rl_robotics (5 JDs) embodied_ai ↔ sim2real (6 JDs) embodied_ai ↔ multi_agent (10 JDs) agent_orchestration ↔ sim2real (2 JDs) pretraining ↔ rl_robotics (3 JDs) frontier_research ↔ rl_robotics (22 JDs) recommender_systems ↔ rl_robotics (16 JDs) code_gen ↔ recommender_systems (6 JDs) code_gen ↔ rl_robotics (4 JDs) rl_post_training ↔ rl_robotics (14 JDs) agent_orchestration ↔ training_infra (3 JDs) rlhf ↔ synthetic_data (19 JDs) multi_agent ↔ vector_db (14 JDs) agent_orchestration ↔ semantic_search (14 JDs) rag ↔ semantic_search (18 JDs) semantic_search ↔ vector_db (10 JDs) llm_observability ↔ semantic_search (14 JDs) evals ↔ semantic_search (3 JDs) model_serving ↔ semantic_search (9 JDs) forecasting ↔ model_serving (39 JDs) search_ranking ↔ vision (25 JDs) fine_tuning ↔ semantic_search (2 JDs) inference_infra ↔ semantic_search (4 JDs) recommender_systems ↔ semantic_search (5 JDs) search_ranking ↔ semantic_search (4 JDs) audio_speech ↔ recommender_systems (22 JDs) audio_speech ↔ search_ranking (12 JDs) code_gen ↔ search_ranking (5 JDs) inference_infra ↔ training_infra (33 JDs) multi_agent ↔ vision (2 JDs) recommender_systems ↔ rlhf (12 JDs) rlhf ↔ search_ranking (11 JDs) agent_orchestration ↔ forecasting (26 JDs) interpretability ↔ recommender_systems (17 JDs) forecasting ↔ inference_infra (17 JDs) forecasting ↔ llm_observability (13 JDs) guardrails ↔ synthetic_data (30 JDs) synthetic_data ↔ vector_db (13 JDs) recommender_systems ↔ synthetic_data (15 JDs) interpretability ↔ search_ranking (13 JDs) search_ranking ↔ synthetic_data (12 JDs) multi_agent ↔ synthetic_data (5 JDs) data_pipeline ↔ llm_observability (4 JDs) data_pipeline ↔ model_serving (4 JDs) data_pipeline ↔ inference_infra (2 JDs) agent_orchestration ↔ copilot (34 JDs) copilot ↔ tool_use (9 JDs) forecasting ↔ recommender_systems (44 JDs) forecasting ↔ search_ranking (10 JDs) copilot ↔ llm_observability (7 JDs) copilot ↔ rag (9 JDs) copilot ↔ vector_db (2 JDs) copilot ↔ model_serving (3 JDs) rag ↔ rl_robotics (8 JDs) rl_robotics ↔ vector_db (8 JDs) embodied_ai ↔ vector_db (8 JDs) recommender_systems ↔ reward_modeling (11 JDs) embodied_ai ↔ recommender_systems (8 JDs) reward_modeling ↔ search_ranking (8 JDs) rl_robotics ↔ search_ranking (12 JDs) embodied_ai ↔ search_ranking (8 JDs) embodied_ai ↔ interpretability (11 JDs) rl_robotics ↔ rlhf (8 JDs) reward_modeling ↔ rl_robotics (8 JDs) embodied_ai ↔ reward_modeling (8 JDs) copilot ↔ evals (2 JDs) copilot ↔ guardrails (4 JDs) agent_orchestration ↔ data_pipeline (3 JDs) code_gen ↔ multi_agent (3 JDs) forecasting ↔ rag (7 JDs) forecasting ↔ vector_db (3 JDs) fine_tuning ↔ forecasting (7 JDs) evals ↔ forecasting (6 JDs) agent_orchestration ↔ distillation (2 JDs) code_gen ↔ rlhf (3 JDs) code_gen ↔ reward_modeling (3 JDs) inference_infra ↔ rlhf (7 JDs) multi_agent ↔ pretraining (5 JDs) model_serving ↔ sim2real (3 JDs) forecasting ↔ multimodal (3 JDs) forecasting ↔ tool_use (3 JDs) forecasting ↔ frontier_research (2 JDs) gpu_kernel ↔ inference_infra (2 JDs) gpu_kernel ↔ model_serving (2 JDs) pretraining ↔ rlhf (5 JDs) pretraining ↔ reward_modeling (2 JDs) distillation ↔ tool_use (2 JDs) evals ↔ training_infra (5 JDs) quantization ↔ tool_use (2 JDs) copilot ↔ fine_tuning (2 JDs) agent_research ↔ forecasting (2 JDs) data_pipeline ↔ rag (2 JDs) data_pipeline ↔ tool_use (2 JDs) Frontier research
N=579 JDs
Top co-occur: Fine-tuning ×259 · Multimodal ×231 · Model serving ×209 Pretraining
N=327 JDs
Top co-occur: Frontier research ×207 · Fine-tuning ×176 · Model serving ×150 Interpretability
N=136 JDs
Top co-occur: Evals ×70 · Frontier research ×65 · Fine-tuning ×53 Multimodal
N=1792 JDs
Top co-occur: Model serving ×938 · Fine-tuning ×722 · Agent orchestration ×668 Fine-tuning
N=3085 JDs
Top co-occur: Model serving ×2113 · Agent orchestration ×1452 · LLM observability ×1342 Fine-tuning Vision
N=871 JDs
Top co-occur: Multimodal ×587 · Model serving ×508 · Fine-tuning ×314 Evals
N=3335 JDs
Top co-occur: Agent orchestration ×2004 · LLM observability ×1787 · Model serving ×1528 Evals RL post-training
N=526 JDs
Top co-occur: Fine-tuning ×356 · Evals ×270 · Model serving ×213 Agent research
N=1060 JDs
Top co-occur: Agent orchestration ×878 · LLM observability ×517 · Evals ×403 LLM observability
N=5416 JDs
Top co-occur: Agent orchestration ×3855 · Model serving ×2756 · RAG ×2357 LLM observability Agent orchestration
N=8032 JDs
Top co-occur: LLM observability ×3855 · Model serving ×3304 · RAG ×2827 Agent orchestration Audio & speech
N=501 JDs
Top co-occur: Model serving ×308 · Fine-tuning ×212 · Multimodal ×197 RL robotics
N=168 JDs
Top co-occur: Embodied AI ×114 · Model serving ×75 · Agent orchestration ×64 Model serving
N=8043 JDs
Top co-occur: Inference infra ×4490 · Agent orchestration ×3304 · LLM observability ×2756 Model serving Embodied AI
N=437 JDs
Top co-occur: Model serving ×172 · Agent orchestration ×131 · RL robotics ×114 Synthetic data
N=390 JDs
Top co-occur: Evals ×153 · Fine-tuning ×144 · Multimodal ×123 Tool use
N=2159 JDs
Top co-occur: Agent orchestration ×2141 · LLM observability ×1297 · RAG ×956 Tool use Guardrails
N=2032 JDs
Top co-occur: Agent orchestration ×1536 · LLM observability ×1442 · Evals ×1001 Guardrails Inference infra
N=4528 JDs
Top co-occur: Model serving ×4490 · Agent orchestration ×1270 · LLM observability ×1113 Inference infra Code gen
N=309 JDs
Top co-occur: Agent orchestration ×220 · LLM observability ×113 · Tool use ×106 RLHF
N=74 JDs
Top co-occur: Fine-tuning ×47 · Evals ×39 · Agent orchestration ×31 RAG
N=3505 JDs
Top co-occur: Agent orchestration ×2827 · LLM observability ×2357 · Model serving ×1920 RAG Vector DB
N=1391 JDs
Top co-occur: RAG ×1228 · Agent orchestration ×996 · LLM observability ×913 Quantization
N=108 JDs
Top co-occur: Inference infra ×102 · Model serving ×100 · Fine-tuning ×45 Reward modeling
N=56 JDs
Top co-occur: RL post-training ×50 · Fine-tuning ×41 · Evals ×40 Search & ranking
N=829 JDs
Top co-occur: Recommender systems ×709 · Model serving ×436 · RAG ×257 Multi-agent
N=178 JDs
Top co-occur: Agent orchestration ×137 · LLM observability ×63 · RAG ×48 Recommender systems
N=1443 JDs
Top co-occur: Search & ranking ×709 · Model serving ×708 · Agent orchestration ×322 Distillation
N=15 JDs
Top co-occur: Model serving ×13 · Inference infra ×12 · Quantization ×12 Training infra
N=60 JDs
Top co-occur: Model serving ×39 · Inference infra ×33 · Multimodal ×8 Sim-to-real
N=6 JDs
Top co-occur: Embodied AI ×6 · RL robotics ×4 · Model serving ×3 Semantic search
N=26 JDs
Top co-occur: RAG ×18 · Agent orchestration ×14 · LLM observability ×14 Forecasting
N=183 JDs
Top co-occur: Recommender systems ×44 · Model serving ×39 · Agent orchestration ×26 Data pipeline
N=19 JDs
Top co-occur: LLM observability ×4 · Model serving ×4 · Agent orchestration ×3 Copilot
N=45 JDs
Top co-occur: Agent orchestration ×34 · Tool use ×9 · RAG ×9 Safety & alignment
N=1 JDs GPU kernels
N=2 JDs
Top co-occur: Inference infra ×2 · Model serving ×2 Agent memory
N=2 JDs 38 tags · 424 co-occurrence edges · min edge weight 2. Bubble area ∝ JDs containing tag · edge thickness ∝ co-occurrence count. Hover any node for top-3 partners; click to see the JDs. Tag velocity · last 4 weeks vs prior 4 Which technologies are hot, which are cooling. Sparkline = 12 weeks of unique roles tagged with each term, last bar on the right is this week. Sorted by absolute pickup. Tags with under 10 lifetime mentions are hidden as noise.