Team　formation,　which　aims　to　form　a　team　to　complete　a　given　task　by　covering　its　required　skills,　furnishes　a　natural　way　to　help　organizers　complete　projects　effectively.　In　this　work,　we　propose　a　new　team　hiring　problem.　Given　a　set　of　projects　P　with　required　skills,　and　a　pool　of　experts　X,　each　of　which　has　his　own　skillset,　compensation　demand　and　participation　constraint　(i.e.,　the　maximum　number　of　projects　the　expert　can　participate　in　simultaneously),　we　seek　to　hire　a　team　of　participation-constrained　experts　T　subset　of　X　to　complete　all　the　projects　so　that　the　overall　compensation　is　minimized.　We　refer　to　this　as　the　participation　constrained　team　hire　problem.　To　the　best　of　our　knowledge,　this　is　the　first　work　to　investigate　the　problem.　We　also　study　a　special　case　of　the　problem,　where　the　number　of　projects　is　within　the　participation　constraint　of　each　expert　and　design　an　exact　algorithm　for　it.　Since　participation　constrained　team　hire　problem　is　proven　to　be　NP-hard,　we　design　three　novel　efficient　approximate　algorithms　as　its　solution,　each　of　which　focuses　on　a　particular　perspective　of　the　problem.　We　perform　extensive　experimental　studies,　on　both　synthetic　and　real　datasets,　to　evaluate　the　performance　of　our　algorithms.　Experimental　results　show　that　our　exact　algorithm　far　surpasses　the　brute-force　solutions　and　works　well　in　practice.　Besides,　the　three　algorithms　behave　differently　when　distinct　facets　of　the　problem　are　involved.