diff --git a/mjx/mujoco/mjx/_src/collision_convex.py b/mjx/mujoco/mjx/_src/collision_convex.py
index 90a709ab..870759ed 100644
--- a/mjx/mujoco/mjx/_src/collision_convex.py
+++ b/mjx/mujoco/mjx/_src/collision_convex.py
@@ -109,106 +109,6 @@ def _closest_segment_point_plane(
   return segment_point
 
 
-def _closest_triangle_point(
-    p0: jax.Array, p1: jax.Array, p2: jax.Array, pt: jax.Array
-) -> jax.Array:
-  """Gets the closest point between a triangle and a point in space.
-
-  Args:
-    p0: triangle point
-    p1: triangle point
-    p2: triangle point
-    pt: point to test
-
-  Returns:
-    closest point on the triangle w.r.t point pt
-  """
-  # Parametrize the triangle s.t. a point inside the triangle is
-  # Q = p0 + u * e0 + v * e1, when 0 <= u <= 1, 0 <= v <= 1, and
-  # 0 <= u + v <= 1. Let e0 = (p1 - p0) and e1 = (p2 - p0).
-  # We analytically minimize the distance between the point pt and Q.
-  e0 = p1 - p0
-  e1 = p2 - p0
-  a = e0.dot(e0)
-  b = e0.dot(e1)
-  c = e1.dot(e1)
-  d = pt - p0
-  # The determinant is 0 only if the angle between e1 and e0 is 0
-  # (i.e. the triangle has overlapping lines).
-  det = a * c - b * b
-  u = (c * e0.dot(d) - b * e1.dot(d)) / det
-  v = (-b * e0.dot(d) + a * e1.dot(d)) / det
-  inside = (0 <= u) & (u <= 1) & (0 <= v) & (v <= 1) & (u + v <= 1)
-  closest_p = p0 + u * e0 + v * e1
-  d0 = (closest_p - pt).dot(closest_p - pt)
-
-  # If the closest point is outside the triangle, it must be on an edge, so we
-  # check each triangle edge for a closest point to the point pt.
-  closest_p1, d1 = math.closest_segment_point_and_dist(p0, p1, pt)
-  closest_p = jp.where((d0 < d1) & inside, closest_p, closest_p1)
-  min_d = jp.where((d0 < d1) & inside, d0, d1)
-
-  closest_p2, d2 = math.closest_segment_point_and_dist(p1, p2, pt)
-  closest_p = jp.where(d2 < min_d, closest_p2, closest_p)
-  min_d = jp.minimum(min_d, d2)
-
-  closest_p3, d3 = math.closest_segment_point_and_dist(p2, p0, pt)
-  closest_p = jp.where(d3 < min_d, closest_p3, closest_p)
-
-  return closest_p
-
-
-def _closest_segment_triangle_points(
-    a: jax.Array,
-    b: jax.Array,
-    p0: jax.Array,
-    p1: jax.Array,
-    p2: jax.Array,
-    triangle_normal: jax.Array,
-) -> Tuple[jax.Array, jax.Array]:
-  """Gets the closest points between a line segment and triangle.
-
-  Args:
-    a: first line segment point
-    b: second line segment point
-    p0: triangle point
-    p1: triangle point
-    p2: triangle point
-    triangle_normal: normal of triangle
-
-  Returns:
-    closest point on the triangle w.r.t the line segment
-  """
-  # The closest triangle point is either on the edge or within the triangle.
-  # First check triangle edges for the closest point.
-  # TODO(robotics-simulation): consider vmapping over closest point functions
-  seg_pt1, tri_pt1 = math.closest_segment_to_segment_points(a, b, p0, p1)
-  d1 = (seg_pt1 - tri_pt1).dot(seg_pt1 - tri_pt1)
-  seg_pt2, tri_pt2 = math.closest_segment_to_segment_points(a, b, p1, p2)
-  d2 = (seg_pt2 - tri_pt2).dot(seg_pt2 - tri_pt2)
-  seg_pt3, tri_pt3 = math.closest_segment_to_segment_points(a, b, p0, p2)
-  d3 = (seg_pt3 - tri_pt3).dot(seg_pt3 - tri_pt3)
-
-  # Next, handle the case where the closest triangle point is inside the
-  # triangle. Either the line segment intersects the triangle or a segment
-  # endpoint is closest to a point inside the triangle.
-  seg_pt4 = _closest_segment_point_plane(a, b, p0, triangle_normal)
-  tri_pt4 = _closest_triangle_point(p0, p1, p2, seg_pt4)
-  d4 = (seg_pt4 - tri_pt4).dot(seg_pt4 - tri_pt4)
-
-  # Get the point with minimum distance from the line segment point to the
-  # triangle point.
-  distance = jp.array([[d1, d2, d3, d4]])
-  min_dist = jp.amin(distance)
-  mask = (distance == min_dist).T
-  seg_pt = jp.array([seg_pt1, seg_pt2, seg_pt3, seg_pt4]) * mask
-  tri_pt = jp.array([tri_pt1, tri_pt2, tri_pt3, tri_pt4]) * mask
-  seg_pt = jp.sum(seg_pt, axis=0) / jp.sum(mask)
-  tri_pt = jp.sum(tri_pt, axis=0) / jp.sum(mask)
-
-  return seg_pt, tri_pt
-
-
 def _manifold_points(
     poly: jax.Array, poly_mask: jax.Array, poly_norm: jax.Array
 ) -> jax.Array: